Conformable Actively Multiplexed High-Density Surface Electrode Array for Brain Interfacing

ABSTRACT

Provided are methods and devices for interfacing with brain tissue, specifically for monitoring and/or actuation of spatio-temporal electrical waveforms. The device is conformable having a high electrode density and high spatial and temporal resolution. A conformable substrate supports a conformable electronic circuit and a barrier layer. Electrodes are positioned to provide electrical contact with a brain tissue. A controller monitors or actuates the electrodes, thereby interfacing with the brain tissue. In an aspect, methods are provided to monitor or actuate spatio-temporal electrical waveform over large brain surface areas by any of the devices disclosed herein.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No. 13/486,726, filed Jun. 1, 2012, which claims the benefit of priority of U.S. Provisional Patent Application No. 61/492,983, filed Jun. 3, 2011, each of which is hereby incorporated by reference herein to the extent not inconsistent with the present application.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under DMI-0328162 awarded by the National Science Foundation, DE-FG02-07ER46471 awarded by the U.S. Department of Energy, NINDS RO1-NS041811, NINDS R01 NS 48598 and 2T32HL007954 awarded by the National Institutes of Health. The government has certain rights in the invention.

BACKGROUND OF INVENTION

Provided herein are methods and devices related to electrophysiological measurement over a complex-shaped biological surface, such as the brain. Specially configured devices are conformable and provide the ability to measure complex spatio-temporal waveforms over relatively large areas of the brain, including in areas requiring high device bendability such as between brain hemispheres.

SUMMARY OF THE INVENTION

Provided herein are biomedical devices and methods of making and using biomedical devices for tissue sensing and actuation applications. For example, flexible and/or stretchable biomedical devices are provided including electronic devices useful for establishing in situ conformal contact with a tissue in a biological environment. The invention includes implantable electronic devices and devices administered to the surfaces(s) of a target tissue, for example, for obtaining electrophysiology data from a tissue such as brain tissue. Also disclosed are methods of sensing and making measurements in a biological environment, including methods of making in vivo electrophysiology measurements.

In one aspect, the invention provides devices for interfacing with a tissue in a biological environment including conformable devices. Devices of this aspect are useful, for example, for sensing and/or actuating a tissue in a biological environment. When placed in a biological environment, devices of an aspect of the invention optionally establish conformal contact with a target tissue(s), thereby providing contact useful for sensing or actuation of the tissue. Further, devices of this aspect optionally maintain conformal contact and/or electrical contact and/or optical communication with the surface of a tissue as the tissue moves and/or as the device is moved across a surface of the tissue. One specific advantage of the devices and methods provided herein is that the conformable aspect of the device establishes good electrical contact over relatively large surface areas, without the drawbacks inherent in various penetrating electrode devices and methods. For example, trauma to the brain is avoided, and concerns related to brain infection and adverse immune response is avoided.

In an aspect, the invention is a method for spatio-temporally electrically interfacing with a brain tissue by providing any of the devices disclosed herein. In an aspect, provided is a conformable device for interfacing with brain tissue in a biological environment, where the device comprises a deformable substrate and a deformable array of electrodes comprising a plurality of electrodes in electrical communication with a plurality of deformable electrical interconnects. A barrier layer encapsulates at least a portion of the deformable electrical interconnects, wherein the deformable substrate, deformable array of electrodes and the barrier layer provide a net bending stiffness of the device low enough that the device establishes conformal contact with the brain tissue in the biological environment, wherein the deformable array of electrodes is supported by the barrier layer. At least a portion of the plurality of electrodes is electrically contacted with the brain tissue in the biological environment by conformally contacting the conformable device with a surface of the brain tissue in the biological environment and spatio-temporally interfacing the brain tissue with the conformable device to monitor or actuate a spatio-temporal profile over the surface of the brain tissue in electrical contact with the plurality of electrodes. In an aspect, the brain tissue is a human brain. In an aspect, the brain tissue is a non-human brain. In an aspect, the biological environment is in vivo.

In an embodiment, the interfacing step further comprises monitoring or actuating the electric potential of the brain tissue at a plurality of individual brain surface locations over a plurality of different time points. In an aspect, temporally adjacent time points are separated by a time period that is greater than or equal to 30 μs and less than or equal to 1 second. In an aspect, the plurality of different time points span a total time period selected from a range that is greater than or equal to 1 second and less than or equal to 80 years or for the remaining lifetime of the patient in which the device is implanted. For example, for acute monitoring or an acute treatment, such as during a surgical procedure, the total time period may be relatively short. In contrast, for chronic monitoring and/or treatment, the total time period may be relatively longer, on the order of days or up to the patient's lifetime. In particular, in aspects where the device is implanted, the monitoring may be continuous over extended periods, or intermittently continuous over select time periods.

In an embodiment, the number of individual brain surface locations is selected from a range that is greater than or equal to 10 and less than or equal to 10,000. In an aspect, the number of locations is selected depending on the application of interest. For example, where detailed information about electrical waveform activity in the brain is desired, monitoring may occur over a larger number of locations, thereby increasing spatial resolution. Similarly, applications requiring precise generation of specific waveforms that may have a complex shape or spatial pattern will correspondingly require a larger number of brain surface electrical contact locations. In contrast, where more simple information, such as an average electrical potential over a large region of the brain, is desired, the number of electrical contact locations is relaxed.

Similarly, the spacing between adjacent brain surface electrical contact locations can be selected depending on the application. In effect, spacing between adjacent brain surface electrical contact locations is governed by spacing between adjacent electrodes in the electrode array. In an embodiment, the adjacent individual brain surface locations are separated from each other by a range that is greater than or equal to 50 μm and less than or equal to 5 mm. There is a relationship between the contact area footprint between the conformable device, the number of electrodes and the electrode spacing (separation distance between adjacent brain surface locations). Interface with an area of the brain requires at least a corresponding contact area footprint and, if detailed information is required, a larger number of electrodes with a small separation distance between adjacent electrodes.

In an aspect, any of the methods provided herein relate analyzing the monitored electric potential spatio-temporal profile to identify an electrical waveform. This aspect is particularly useful for identifying potential abnormalities in brain function, such as may arise from disorders associated with defects in particular areas of the brain. In an embodiment, the analyzing step comprises pattern recognition, a clustering algorithm; machine learning, or a combination thereof. For example, the machine learning relate to monitoring brain activity in a certain location, corresponding to an individual's state, such as a normal state sleeping, awake, restful, active, Similarly, the activity at the location may correspond to an adverse event such as a seizure, Parkinson's tremor, depression. In this fashion, waveforms identified as normal or abnormal for that individual at that location are quantified or “learned”. This information can then be used in various numerical or other algorithms to identify waveform state under conditions that are not controlled.

In an aspect, the analyzing step comprises monitoring the magnitude of electric potential at each brain surface location, a time course of electric potential change at each brain surface location, or both. In this fashion, detailed information about the waveform is obtained, including an electrical waveform that changes over time and space. In another aspect, the analyzing step further comprises calculating the relative delay of a spike in electrical potential at a brain surface location, wherein a spike is identified for any brain surface location having an electric potential that is greater than 50% of a peak root-mean-square value over all brain surface locations. Alternatively, the spike is calculated on the basis of exceeding an average value at that specific location, such as the average value under normal brain activity conditions.

In an embodiment, the method further comprises encoding a magnitude and/or speed and direction of the waveform from the relative delay and electric potential at each brain surface location. The speed and direction can be calculated using any number of algorithms. For example, if delay times are determined, delay times at various locations can be used to calculate speed and direction of the electric depolarization over the brain surface. Similarly, recording the magnitude of potential at different locations at different times allows one to determine the speed and direction of the electrical waveform and, accordingly, identification of the waveform as abnormal or normal. Accordingly, another embodiment of the invention relates to analyzing the encoded waveform and identifying the waveform as abnormal or normal.

The analyzing step may further comprise any number of techniques known in the art. In an aspect, the analyzing step further comprises principal components analysis (PCA) to reduce the computational requirement of the analyzing step.

Any of the methods provided herein optionally further comprise the step of actuating a spatio-temporal electrical profile over the surface of the brain tissue to disrupt the abnormal waveform. The actuation of a profile over the surface of the brain comprises energizing the plurality of electrodes so that a voltage pattern is generated over the electrode array. In an aspect, each electrode is capable of energization in a time-dependent fashion. In this manner, because there is an electrical connection between each electrode and each brain surface location underlying the electrode, complex spatio-temporal waveforms can be generated on the brain surface from the corresponding time-dependent energization of electrodes. Given the device can have a large number of electrodes, closely spaced, with a high temporal resolution control, extremely fine voltage profiles that can rapidly change with time are obtained, thereby providing high-spatial and temporal spatio-temporal electrical waveforms along the brain surface and underlying regions thereof. The energizing refers to independently energizable electrodes in the electrode array. “Independently” refers to both the magnitude of voltage and the time-dependency of voltage magnitude being independently controlled for each electrode.

In an aspect, the actuating step comprises energizing the plurality of electrodes with a pattern of electric potential having a stimulation profile that stimulates the brain in a pre-emptive manner to cause the neural tissue to be in a refractory state prior to the arrival of the next wave. This prevents the continuation of an ongoing seizure pattern. Other useful actuating steps relate to energization of electrodes to generate a region of high frequency stimulation or depolarization sufficient to at least partially terminate propagation the abnormal waveform, or to generate a polarity profile corresponding to a waveform of normal brain activity.

In an embodiment, any of the methods are useful for taking action when an abnormal waveform is detected. In an embodiment, this action is energizing the electrodes to generate a voltage profile or a spatio-temporal voltage waveform on the brain surface as discussed above. Another action relates to activating a therapeutic device when an abnormal waveform is identified, wherein the therapeutic device applies a therapeutic intervention to the brain. This therapeutic intervention may be distinct from energization of the plurality of electrodes. For example, the therapeutic device and therapeutic intervention may be one or more of: a penetrating electrode to provide electrical stimulation; a micro-syringe to inject a chemical compound onto or into the brain; or an optical fiber to optically stimulate the brain surface or brain interior.

In another aspect, any of the methods relate to detection or actuation of specific waveform types, such as identifying as abnormal a waveform comprising a plane wave propagating in a first direction that bends and subsequently propagates in a reentrant spiral loop having a preferential direction that is clockwise or counter-clockwise. Furthermore, the abnormal waveform may be identified as having multiple distinct waveforms, such as a second plane wave that changes the direction of the reentrant spiral loop waveform. One advantage of the devices and methods provided herein is that these different waveforms can be detected with a sensitivity that even permits detection of multiple distinct waveforms over the brain surface that interact with each other. Conventional techniques, in contrast, may not be able to distinguish such waveforms, and certainly not a waveform consisting of a plurality of distinct waveforms.

Depending on the waveform monitored by the device, appropriate counter-action may be taken, such as spatio-temporally energizing the plurality of electrodes to actuate the brain tissue surface with a traveling wave of electric potential to terminate a reentrant spiral loop. Given the device is useful for both monitoring and actuation brain spatio-temporal waveforms, repeated monitoring and actuation may be taken with different actuation waveforms if an initial actuation waveform does not elicit a satisfactory response in the brain (e.g., termination of an undesirable waveform and generation of a normal brain state as reflected by brain activity monitored by the device).

Other spatio-temporal profiles of interest correspond to an interictal or an ictal state, a cluster of spikes, a spindle oscillation that is punctate and temporally coherent, a waveform that is at least partially a substantially planar wave having a preferential propagation direction, and/or a waveform that is a spiral wave.

In an embodiment, any of the methods may be used to detect a spiral wave waveform, thereby providing indication of ictal onset, including warning of a potential seizure episode.

In an aspect, the interfacing step further comprises identifying the spatio-temporal profile as a spiral waveform and actuating electrical activity over the brain surface with an output spatio-temporal profile from the deformable array of electrodes to disrupt or terminate the spiral waveform. This is particularly useful for applications wherein the spiral waveform disruption prevents, attenuates, or stops a seizure.

In an aspect, any of the methods provided herein relate to implanting the conformable device in a patient. In an aspect, the patient is a human. In an aspect, the patient is not a human.

The ability to provide conformable devices having good foldability provides methods wherein at least a portion of the conformable device is inserted into a sulcus or a groove of the brain tissue without penetrating through brain tissue. This aspect acknowledges that the brain surface has a number of invaginations where it may be desired to measure electrical waveforms within the invagination so that, although the device is inserted in brain invaginations (e.g., groove, sulcus), the outer-most membrane defining the brain surface is itself not penetrated. In this aspect, the conformable device can be two-sided to monitor or actuate two spatio-temporal profiles, a first spatio-temporal profile from one side of the sulcus or groove, and a second spatio-temporal profile from a second side of the sulcus or groove. In an embodiment, the two sided device is made by folding the conformable device to provide the two-sides from a single deformable device.

The conformable nature of the device provides access to interfacing with very different locations of the brain surface simultaneously, for example within invaginations and outside the invaginations. Accordingly, an aspect of the invention relates to electrical contact by conformal contact with at least a portion of a sulcus or groove of the brain tissue and at least a portion of a gyrus of the brain tissue.

In an aspect, any of the methods provided herein relate to both monitoring and actuating brain tissue. The configuration of the device and electrodes of the device permit sensing or monitoring of electric potential over the brain surface by the electrodes and/or actuation of electric potential over the brain surface by energization of the electrodes.

In an embodiment, any of the methods further comprise connecting the conformable device to a neuroprosthetic device. This embodiment has particular applications for individuals suffering a neurological disorder or a paralysis where signal from the brain is disrupted. In this case, the nerve-conduction signal between the brain and corresponding body part controlled by the brain can be bypassed with brain output sensed by the conformable device and used to send a corresponding signal to the neuroprosthetic device. Alternatively, the brain signals can be decoded and directly used as an input system for a computer or a communication device. For example, a cursor can be controlled that allows a paralyzed patient to read and write such as email. The devices and methods provided herein are particularly suited to these applications given their relatively high information transfer rates. Examples of neuroprosthetic devices include the BrainGate™ Neural Interface System.

In an aspect, conformal contact comprises physical contact with a surface of the brain tissue, without penetrating the brain tissue.

Any of the devices used in the method may be further characterized by a number of physical parameters. In an aspect, the conformable device is bendable, stretchable, or both bendable and stretchable. The bendable aspect is particularly relevant for applications where the conformable device is at least partially inserted into a brain surface invagination and where the device is folded over a support material to provide two-sided interfacing capability.

Other relevant physical parameters relate to methods having a certain spatial and/or temporal resolution. In an aspect, the spatial resolution is selected from a range that is greater than or equal to 50 μm and less than or equal to 5 mm; and the temporal resolution selected from a range that is greater than or equal to 30 μs and less than or equal to 1 second. Spatial resolution is selected by adjusting the spacing or separation distance between adjacent electrodes, such as center-to-center distance or edge-to-edge distance. Temporal resolution is selected by adjusting the frequency at which the electrodes are monitored and/or energized.

In an embodiment, any of the methods provided herein interface with a large area of brain tissue, as reflected by the large conformable contact area footprint with the brain surface. In an aspect, the conformable contact area footprint is selected from a range that is greater than or equal to 10 mm² and less than or equal to 100 cm².

In an aspect, the barrier layer limits a net leakage current from the deformable array of electrodes to the tissue to an amount that does not adversely affect the tissue.

In an aspect of the invention, barrier layers include moisture barriers. In one embodiment, the barrier layer is configured to limit a net leakage current from the electronic device to the biological environment to less than 10 μA, optionally for some applications less than 5 μA and optionally for some applications less than 1 μA, and optionally for some applications less than 0.1 μA. In some embodiments, the barrier layer prevents leakage current from being concentrated to small areas so to prevent tissue damage caused by current leakage from the device. In an embodiment, for example, the barrier layer is configured to limit leakage current from the device to the biological environment to 0.1 μA/cm²; less, and for some applications 0.01 μA/cm² or less, and for some applications 0.001 μA/cm² or less. In some embodiments, barrier layers of the invention have an electrical resistivity of 10¹⁴ Ω·m or greater, for example an electrical resistivity selected over the range of 10¹⁵ to 10¹⁷ Ω·m. In some embodiments, the barrier layer prevents the rate at which charge is leaked from the electronic device; for example, one barrier layer embodiment limits electrical discharge from a device to 10 μC or less over a period of 1 second or 10 μA. In some embodiments, the barrier layer limits leakage current or average leakage current from the device to 10 μA or less or 5 μA or less over a long period of time, such as 3 hours or more or 5 hours or more. In an embodiment, any of the devices or methods provided herein relates to monitoring net leakage current, and if the net leakage current exceeds a specified maximum value, the device is shut down. In an embodiment, the maximum value corresponds to a total charge value, such as 10 μA or greater over any 1 second interval. Similarly, the maximum value may correspond to a lower current, but over a greater time interval, such as a sustained leakage of 1 μA over 1 minute, or 0.1 μA over one hour. The maximum value can be selected to correspond to a value above which tissue is adversely affected, including a permanent or a temporary affliction.

In some embodiments, a barrier layer is configured to prevent moisture from reaching the flexible or stretchable electronic circuit and limit leakage current therefrom, for example to less than 10 μA optionally for some applications less than 5 μA and optionally for some applications less than 1 μA. Useful moisture barriers, for example, include those configured for protecting tissue in contact with electronic device embodiments from damage due to leakage current. Further, useful moisture barriers include those configured for protecting electronic devices from damage due to leakage current.

In an embodiment, the barrier layer is patterned so as to selectively modulate physical, thermal, optical, electromagnetic and/or electrical contact and/or communication between flexible semiconductor circuit elements and the tissue in the biological environment. Optionally, a barrier layer comprises multiple layers. For example, a barrier layer comprises at least one organic polymer layer and at least one inorganic dielectric layer. In specific embodiments, the net thickness of a barrier layer comprising multiple layers is selected over the range of 1 μm to 25 μm or over the range of 1 μm to 100 μm.

In some embodiments, the barrier layer includes one or more vertical interconnect access (via) structures. As used herein, a via structure refers to a recessed region which is at least partially filled with a conducting material. Via structures are useful in a barrier layer for providing electrical communication between electronic circuit components encapsulated by a barrier layer (e.g., semiconductor device such as a transistor, amplifier or multiplexer) and electronic circuit components not encapsulated by a barrier layer and in contact with the tissue or fluid in contact with the tissue (e.g., an electrode). In a specific embodiment, the barrier layer comprises multiple layers and includes multiple offset via structures; for example, one via structure in a lower barrier layer and one via structure in an upper barrier layer in electrical communication with the first via structure. In embodiments, barrier layers including multiple layers with offset via structures are useful as moisture barriers.

Depending on the application, the barrier layer can have a variable thickness; that is, for certain applications, the barrier layer has a thickness that is spatially variable (i.e., relatively thicker in some regions and relatively thinner in other regions). In embodiments where a sensing element does not need to be exposed and/or in direct contact with or electrical communication with a tissue in a biological environment, barrier layers of spatially varying thickness are useful; for example, when a sensing element is positioned close to the surface (e.g., within 5 μm or less) of the barrier layer but still encapsulated by the barrier layer.

Also provided herein are devices. In an aspect, the device is useful in performing any of the methods disclosed herein. In an embodiment, the device is for spatio-temporally electrically interfacing with a brain in a biological environment. In an aspect, any of the devices or methods provided herein are used to monitor a spatio-temporal waveform on the brain surface, including identifying waveforms considered abnormal and, optionally, taking action to abrogate such abnormal waveforms.

In an embodiment, the device comprises a conformable substrate and a conformable electronic circuit comprising a deformable array of electrodes in electrical communication with a plurality of deformable electrical interconnects. The deformable array of electrodes are supported by the conformable substrate, such as by an intervening layer that is a barrier layer that is itself supported by the conformable substrate. The barrier layer encapsulates at least a portion of the deformable electrical interconnects, wherein the conformable substrate, conformable electronic circuit and the barrier layer provide a net bending stiffness of the device low enough that the device establishes, or is capable of establishing, conformal contact with brain tissue in the biological environment. A controller is connected to the conformable electrical circuit to monitor or actuate a spatio-temporal profile over the surface of the brain in electrical contact with the plurality of electrodes. In this aspect, each electrode is independently addressable, to provide independent electrical stimulation.

In an aspect, each electrode is electrically connected to a pair of transistors, wherein the transistors comprise a multiplexing transistor and a buffer transistor. In this aspect, a unit cell of the device comprises an electrode and two transistors, with electrical interconnects electrically connecting the electrode to the transistors in series, and electrical interconnects connecting the transistors to power, ground and to the controller. In an embodiment, each pair of transistors is electrically connected to a common constant current source and a current mirror. In an aspect, the constant current source and current mirror are considered components of the controller, where the controller portion is “off-chip” and away from the conformable substrate. The controller may include analog amplification or buffering, filtering and analog to digital conversion. The controller may also include digital signal processing, pattern recognition and programmable stimulation. In an embodiment, the pair of transistors are matched transistors. In an embodiment, the pair of transistors are not matched transistors.

In an aspect, the transistors are flexible and comprise single-crystal silicon.

In an embodiment, adjacent electrodes are separated from each other by a separation distance selected from a range that is greater than or equal to 100 μm and less than or equal to 1 mm. The separation distance may be described in terms of center-to-center distance or edge-to-edge distance of adjacent electrodes. In an aspect, the separation distance is constant. In an aspect, the separation distance varies, such as lower separation distance in central region of the device (e.g., where high spatial resolution is desired) and higher separation distances around the edges (where high spatial resolution may not be required, but large-area monitoring is desired). In an aspect, the separation distance may be described in terms of an average separation distance, with minimum and maximum separation distances that vary less than or equal to 100% of average, less than or equal to 50% of average, or within about 20% of average.

In an aspect, the device is characterized in terms of an electrode surface area. In an embodiment of this aspect, each electrode has an electrode surface area that is less than or equal to 0.1 mm², and optionally, greater than or equal to 0.01 mm².

In an embodiment, the deformable array of electrodes is supported by a top surface of the barrier layer. In an aspect, the electrode is partially embedded in the barrier layer, such that a top surface of the electrode is not covered by the barrier layer. In an aspect, the electrodes are coated with an electrode coating layer, such as an electrode coating layer comprising platinum. In an aspect, the electrode, electrode coating layer and barrier layers are biologically inert, in that the materials do not illicit a significant immune response.

In an aspect, the device is described in terms of a thickness. In an embodiment, the thickness is less than or equal to 30 μm, or less than about 25 μm. Such small thicknesses are particularly relevant given the surface geometry of the brain comprises multiple folds, grooves and invaginations requiring the conformable device to have large bendability or foldability.

Any of the devices provided herein may further comprise a support material having a first surface and a second surface opposed to the first surface, wherein a first portion of the electrodes are supported by the first surface, and a second portion of the electrodes are supported by the second surface. Such a device is particularly suited for insertion between brain hemispheres, or other invaginations or grooves, where an interface is desired with both surfaces simultaneously.

In an aspect, any of the devices, as well as methods, are particularly suited for therapeutic applications beyond electrical waveform actuation by the electrodes of the device. In an embodiment, the device further comprises a therapeutic device operably connected to the controller and a receiving passage through the conformable substrate for receiving at least a portion of the therapeutic device. In this aspect, the controller is configured to actuate the therapeutic device to provide a therapeutic intervention to the brain. For example, in response to detection of an abnormal waveform or other abnormality, the controller may send a signal to the therapeutic device to provide a therapeutic intervention. The therapeutic intervention may itself be a physical parameter such as an electrical stimulation or shock, heat, or electromagnetic radiation. Alternatively, the therapeutic intervention may be chemical in nature, such as by selected application of a chemical or biological agent to a select region of the brain. In an aspect, the therapeutic device is a penetrating electrode, a micro-syringe, or an optical fiber. Other examples of therapies include ablation therapy, such as by locally applied temperature variation (e.g., hypothermic or hyperthermic), electrical stimulation, optical stimulation and radiofrequency (RF).

In an embodiment, the receiving passage is centered at a center point of the conformable substrate, and the receiving passage has a cross-sectional area selected from a range that is greater than or equal to 100 μm² and less than or equal to 1 cm². In an aspect, multiple receiving passages are provided through the device, thereby providing the ability to selectively adjust the position at which the therapeutic is delivered.

In another aspect of the invention, provided is a method of identifying an abnormal spatio-temporal brain waveform in a subject using any of the devices provided herein. The spatio-temporal electrical brain waveform is monitored with any of the devices provided herein that are in conformal and electrical contact with a brain surface of the subject. The monitoring comprises detecting the electric potential of a plurality of individual brain surface locations beneath each electrode of the array of electrodes at a plurality of different time points. The monitored electric potential is encoded to obtain an encoded spatio-temporal electrical brain waveform. For example, useful encoded parameters include brain electrical potential as a function of time over the brain surface locations underlying the plurality of electrodes. The encoding may be on-chip or may be transmitted to a recording device or display for further analysis. In an aspect, the encoded waveform provides information about parameters useful for identifying a waveform to provide classification as to brain state, including abnormalities. Examples of useful parameters include waveform speed, direction, intensity, and brain locations thereof. From the encoded waveform, and parameters calculated therefrom, analysis as to abnormality can occur. For example, the analyzing step can be analyzing the encoded waveform to identify an abnormal spatio-temporal brain profile.

In an aspect, the abnormal spatio-temporal brain profile is identified from a waveform that is a spiral waveform.

Optionally, the method further comprises actuating the array of electrodes with a spatio-temporal electric potential profile and generating a waveform of electric potential to the brain surface from the actuated array of electrodes to disrupt or terminate the abnormal spatio-temporal brain profile. For example, the abnormal waveform may comprise a spiral waveform, and the termination may relate to electrode actuation with a counter-stimulus to cancel the unwanted spatial pattern, thereby disrupting the abnormal waveform. Alternatively, select regions of the brain may be stimulated to counter-act physical symptoms associated with an abnormal waveform, such as a seizure.

In an embodiment, the method further comprises the step of implanting the device in the subject by conformally contacting the device with the brain surface without physical penetration of brain tissue. Such an implantation may be endoscopically, especially given the device is conformable, foldable, bendable and stretchable. The monitoring with the device may be acute, such as during an operative procedure or a test (e.g., on the order of 12 hours or less), or may be chronic, such as over a time period that is greater than 12 hours.

In an aspect, any of the methods provided herein are directed to a method to obtain data, such as electrical spatio-temporal waveform over brain tissue. In this aspect, the technique of applying the device against brain may be considered a minor intervention, involving a safe, routine technique of conformal application of an electrode array over a patient, with a device monitoring brain waveform activity, without any physical disruption to the brain. Accordingly, for any of the methods provided herein that recite a surgical treatment step, the surgical treatment step is optionally disclaimed, including in jurisdictions that consider such surgical treatment steps to be prohibited patentable subject matter. In an aspect, any of the devices provided herein are used to measure or monitor electrical activity over a biological surface, such as the brain surface.

The devices and methods provided herein facilitate significantly higher information transfer rates between the brain surface and the device conformally contacted thereto than is possible with current state-of-the art devices. This provides unique capabilities with respect to brain-machine interfaces. Accordingly, any of the devices and methods provided herein may be further characterized in terms of a minimum data transfer rate or “sampling transfer”, such as a rate determined by the size of the array and the sampling and/or actuation frequency. In an aspect, the minimum data transfer rate is described in terms of the number of samples per second, with each sample corresponding to a value at one electrode at one point in time. For a 360 electrode array, with a temporal resolution of 0.5 ms, this would correspond to a data transfer rate of 720,000 samples per second. In this aspect, the minimum data transfer rate is optionally, greater than or equal to 90,000 samples per second, greater than or equal to 360,000 samples per second, or greater than or equal to 3,600,000 samples per second.

The capacity for such high rates of data transfer and bandwidth provides access to a brain diagnostic tool that provides information at a rate that is greater than or equal to 90,000, 360,000 or 3,600,000 samples per second. In an embodiment, this information is electric potential of the electrodes in the electrode array, corresponding to the electric potential of the brain surface at a location matched to the electrode that overlies that brain surface location.

Without wishing to be bound by any particular theory, there can be discussion herein of beliefs or understandings of underlying principles or mechanisms relating to embodiments of the invention. It is recognized that regardless of the ultimate correctness of any explanation or hypothesis, an embodiment of the invention can nonetheless be operative and useful.

DESCRIPTION OF THE DRAWINGS

FIG. 1A is a top view schematic of a device for spatio-temporally electrically interfacing with a brain. FIG. 1B is a side view. FIG. 1C is a device for monitoring and actuating brain tissue.

FIG. 2A-2F. Flexible, high-resolution multiplexed electrode array. FIG. 2A, Photograph of a 360 channel high density active electrode array. The electrode size and spacing (center-to-center) is 300 μm×300 μm and 500 μm, respectively. (inset) A closer view showing a few unit-cells. FIG. 2B Schematic circuit diagram of single unit-cell containing two matched transistors (left) (labeled buffer and multiplexer), transfer characteristics of drain-to-source current (Ids) from a representative flexible transistor on linear and logarithmic scales as gate to source voltage (V_(gs)) is swept from −2 to +5 V, demonstrating the threshold voltage (V_(t)) of the transistor (center). Current-voltage characteristics of a representative flexible silicon transistor (right). I_(ds) is plotted as a function of drain-to-source voltage (V_(ds)). V_(gs) is varied from 0 to 5 V in 1-V steps. FIG. 2C, Schematic exploded view (left) and corresponding microscope image of each layer: doped silicon nanoribbons (right frame, bottom), after vertical and horizontal interconnection with arrows indicating the 1st and 2nd metal layers (ML) (right frame, 2nd from bottom), after water-proof encapsulation (“barrier layer”) (right frame, 3rd from bottom) and after platinum electrode deposition (right frame, top). Green dashed lines illustrate the offset via structure, critical to preventing leakage current while submerged in conductive fluid. FIG. 2D, Images of folded electrode array around low modulus Polydimethylsiloxane (PDMS) insert. FIG. 2E, bending stiffness of electrode array for varying epoxy thicknesses and two different PI substrate thicknesses. A nearly 10-fold increase in flexibility between the current device and our prior work is shown. FIG. 2F, Induced strain in different layers depending on the change in bending radius.

FIG. 3A-3B. Animal experiment using feline model. FIG. 3A, A flexible, high-density, active electrode array placed on the visual cortex. (inset) The same electrode array inserted into the interhemispheric fissure. FIG. 3B, Folded electrode array before insertion into the interhemispheric fissure (left). Flat electrode array inserted into the interhemispheric fissure (right).

FIG. 4A-4B. Spontaneous barbiturate-induced sleep spindles. FIG. 4A, A typical spindle recorded from a representative channel. Negative is plotted up by convention. Arrows point to individual spikes of the spindle (I-IV) further analyzed in the following panel. FIG. 4B, Root-mean-square (RMS) value of the zero-meaned signal of individual sharply contoured waves comprising the spindle demonstrate high sensitivity of the electrode array and the spatially-localized nature of spindles (left column) as well as the high degree of temporal synchronization indicated by the relative time to peak across the array (right column). Data are anatomically orientated as shown in the inset of FIG. 5B.

FIG. 5A-5C. Visual evoked response analysis to a 2-dimensional sparse noise visual stimulus. FIG. 5A, 64 color maps, each showing the response (root-mean-square (RMS) value of the zero-meaned signal within the response window) of the entire 360 channel electrode array. The color maps are arranged in the same physical layout as the stimuli are presented on the monitor, i.e. the image map in the upper left hand corner of the figure represents the neural response across the entire array to a flashing box presented in the upper left hand corner of the monitor. The color scale is constant over all 64 image maps and is saturated at the 1st and 99th percentile respectively to improve the visual display. FIG. 5B, 64 color maps generated from the same response data as in a, but plotting the response latency in ms. Channels that did not show a strong response, as determined by exceeding 50% of the maximum evoked response, were excluded and are colored white. (inset) Exploded view illustrates the anatomical orientation of the electrode array on the brain and approximate location of Brodmann's areas (grey numbers and dashed lines). FIG. 5C, Performance results achieved after subjecting a test set of data to a deep belief net classifier in accurately determining each originating location on the screen of respective stimuli. 23 of the 64 screen locations (36%) were predicted exactly correct (black boxes), significantly better than chance (1.6%). 41 of 64 (64%) screen locations were predicted correctly within one neighboring square (grey boxes, distance≦√2, chance level 14.1%).

FIG. 6A-6F. Detailed 2-dimensional data from electrographic seizures in feline neocortex. FIG. 6A, μECoG signal from a representative channel of the electrode array during a short electrographic seizure. Negative is plotted up by convention. Labelled segments correspond to movie frames below. FIG. 6B, Movie frames showing varied spatial-temporal μECoG voltage patterns from all 360 electrodes during the labeled time intervals from FIG. 6A. The frame interval and color scale are provided for each set of 8 movie frames and the color scale is saturated at the 2nd and 98th percentile respectively over 8 frames to improve the visual display. Data are anatomically orientated as shown in the inset of FIG. 5B. FIG. 6C, Relative delay map for the 4 to 8 Hz band-pass filtered data from 3 seconds of continuous counter-clockwise spiral rotations (FIG. 6B, waveform IV) illustrating a clear phase singularity and counter clockwise rotation. FIG. 6D, Relative delay map for narrow band-pass filtered data from ˜0.5 seconds of clockwise spiral rotations (FIG. 6B, waveform II) illustrating clockwise rotation, but a less clear singularity. FIG. 6E, Representative delay image maps from six different spike clusters are shown to illustrate the differences between clusters (left columns). The average waveform for the corresponding spike (red traces, right columns) illustrates that complicated spatial patterns at the micro scale (0.5 mm) can be indistinguishable at the current clinical scale (˜10 mm). Numerals I, III and V indicate the clusters that the corresponding waves in FIG. 6B belong to. FIG. 6F, Representative delay image maps from two clusters that occurred almost exclusively during seizures, illustrating striking differences in spatial-temporal micro scale patterns during seizures.

FIG. 7A-7B. FIG. 7A, Microscope images of each fabrication step. FIG. 7B, schematic cross-sectional information, dotted line shows the location of a neutral mechanical plane (NMP).

FIG. 8A-8B. A single-trial visual evoked potential from a full-field drifting grating. FIG. 8A, Spatial distribution of the visual evoked response, as determined by the root-mean-square (RMS) value of the zero-meaned signal within the 40 ms to 160 ms window after the stimulus. Data are anatomically orientated as shown in the inset of FIG. 5B. FIG. 8B, Individual visual evoked responses shown for the 49 electrodes located in the bottom, left-hand corner of the electrode array, as highlighted by the dashed box above. This is an example of a spatio-temporal profile over 49 separate locations. Similar profiles can be obtained over the entire device footprint (e.g., 360 locations in an 18×20 electrode array).

FIG. 9. Analysis of the frequency content of a sustained, counter-clockwise spiral during a short seizure. The primary frequency component was 6 Hz. The power spectral density is calculated using ‘pwelch’ with a window size of 1024 on each channel of the electrode array individually. The resulting power spectra are averaged to produce a single overall power spectral density.

FIGS. 10A-19B and 20. Delay maps for all of the spikes in each cluster indicated a strong similarity within clusters. The spikes in clusters 2, 4, 12, 14, and 19 appeared to occur almost exclusively during seizures, while spikes in the other clusters appeared to occur uniformly throughout the record.

FIG. 21. Circuit diagram of four unit cells, showing multiplexing connections.

FIG. 22A-22C. FIG. 22A, Photograph of custom circuit board that implements the off array constant current sinks, buffering and high-pass filtering. FIG. 22B, Photograph of custom data acquisition interface circuit board that generates row select signals and provides another stage of buffering (top) and FIG. 22C, (bottom).

FIG. 23. Block diagram of constant current sink implementation. This circuit is repeated 20 times, one for each column of the electrode array.

FIGS. 24-31. Schematics of the custom data acquisition interface circuit board.

FIG. 32A-32B. FIG. 32A, A diagram that shows parameter definitions for insertion model of folded electrode array. FIG. 32B, Strain induced in the brain during insertion of the folded electrode array for two different brain hemisphere spacings.

FIG. 33. Density map illustrating the spatial distribution of the electrode response to a 100 mV p-p, 3.14 Hz sine wave, demonstrating the spatial uniformity of the gain of the electrode array.

FIG. 34. Photograph of a 360 channel, high density neural electrode array used in a feline model of epilepsy. The electrode array is placed on the surface of visual cortex. The electrode size and spacing is 300 μm×300 μm and 500 μm, respectively.

FIG. 35. Representative spike waveforms retained by the spike detection algorithm. Each of the spike waveforms is 160 ms clipped from the average voltage recording of all 360 channels. Within each window there is only one negative-going crossing followed by one positive-going crossing. These 16 waveforms are of the spikes closest (in the L1 sense) to each of the cluster centroids and correspond to the spatio-temporal delay maps in FIG. 36. Negative is plotted up by convention.

FIG. 36. Delay maps for the 16 clusters. Each of the above delay maps represents the spike within each cluster that is closest to its cluster centroid. Blue indicates electrodes with an early delay value relative to the average spike waveform and red indicates electrodes with a late delay value. Color shading represents relative timing of peak voltage in each spike, but not speed of propagation. As an example, the delay map for cluster 1 displays a spike ST pattern of propagation across the array of a spike that enters on the bottom left and proceeds in a sweeping arc until it exits the array in the top left. The corresponding RMS maps have not been included due to space constraints.

FIG. 37. Pie charts representing the 16 identified clusters and their proportion of spikes during and outside of seizures. Each of the pie charts represents one of the 16 different spike ST patterns identified. The whole area of each pie is scaled in proportion to the total number of spikes within the cluster. The blue section within each pie is the percentage of spikes associated with non-seizure epochs. The red section within each pie is the percentage of spikes recorded during seizures.

FIG. 38. Top panel shows a high density neural (HDN) sensor array with a passage for receiving a therapeutic device. The array has 500 μm electrode spacing with a 9 mm by 10 mm area coverage. The bottom panel is a large area HDN array comprising 32 by 32 unit cells (1024 total cells) with a 16.2 mm×16.2 mm area coverage.

DETAILED DESCRIPTION OF THE INVENTION

In general, the terms and phrases used herein have their art-recognized meaning, which can be found by reference to standard texts, journal references and contexts known to those skilled in the art. The following definitions are provided to clarify their specific use in the context of the invention.

“Spatio-temporally” or “spatio-temporal” refers to a parameter having a spatial pattern which may change over time. For example, electric potential over the brain surface changes with time, with different regions of the brain generating or propagating an electric potential under various conditions. A sleeping patient will have a different spatio-temporal waveform than an active person. A patient having a brain-generated seizure has a different spatio-temporal waveform than a patient not seizing. Accordingly, “spatio-temporally electrically interfacing” refers to spatial and temporal electrical connection between a device and a brain tissue, so that the spatio-temporal monitoring and/or actuation of the brain can occur.

“Electrically interfacing” refers to the ability to monitor and/or generate electrical waveforms on the brain surface in regions underlying the device electrodes.

“Brain tissue” refers to brain in the in vivo, in vitro, or the ex vitro environment. The brain may be from a human or a non-human, such as an animal.

“Conformable” refers to a device, material or substrate which has a bending stiffness sufficiently low to allow the device, material or substrate to adopt a desired contour profile, for example a contour profile allowing for conformal contact with a surface having a pattern of relief or recessed features. In certain embodiments, a desired contour profile is that of a tissue in a biological environment, for example heart tissue. “Deformable” is used similar to conformable, and refers to a device, material or substrate can flex, bend, or conform without undue induced strain during deformation, specifically an induced strain below that required to induce mechanical fracture or permanent fatigue. In particular, the element is considered deformable if any induced stress associated with deformation is below the ultimate tensile stress or the yield stress.

“Stretchable” refers to the ability of a material, structure, device or device component to be strained without undergoing fracture. In an exemplary embodiment, a stretchable material, structure, device or device component may undergo strain larger than 0.5% without fracturing, for some applications strain larger than 1% without fracturing and for yet other applications strain larger than 3% without fracturing. As used herein, many stretchable structures are also flexible. Some stretchable structures (e.g., device components) are engineered to be able to undergo compression, elongation and/or twisting so as to be able to deform without fracturing. Stretchable structures include thin film structures comprising stretchable materials, such as elastomers; bent structures capable of elongation, compression and/or twisting motion; and structures having an island-bridge geometry. Stretchable device components include structures having stretchable interconnects, such as stretchable electrical interconnects.

“Electrical communication” refers to an arrangement of two components of a device wherein electrical signal (e.g., current, potential) is passed between the two components. For example, each electrode in the array is electrically connected to a pair of transistors, and the transistors are connected to a current source or sink, and specifically, to a controller. The parts of the device that convey the electrical signal between the electrical components are herein referred to as “interconnects”.

A “component” is used broadly to refer to a material or individual component used in a device. An “interconnect” is one example of a component and refers to an electrically conducting material capable of establishing an electrical connection with a component or between components. In particular, an interconnect may establish electrical contact between components that are separate and/or can move with respect to each other. Depending on the desired device specifications, operation, and application, an interconnect is made from a suitable material. For applications where a high conductivity is required, typical interconnect metals may be used, including but not limited to copper, silver, gold, aluminum and the like, and alloys. Suitable conductive materials further include semiconductors, such as silicon and GaAs and other conducting materials such as indium tin oxide.

An interconnect that is “stretchable” or “flexible” is used herein to broadly refer to an interconnect capable of undergoing a variety of forces and strains such as stretching, bending and/or compression in one or more directions without adversely impacting electrical connection to, or electrical conduction from, a device component. Accordingly, a stretchable interconnect may be formed of a relatively brittle material, such as GaAs, yet remain capable of continued function even when exposed to a significant deformatory force (e.g., stretching, bending, compression) due to the interconnect's geometrical configuration. In an exemplary embodiment, a stretchable interconnect may undergo strain larger than 1%, optionally 10% or optionally 30% or optionally up to 100% without fracturing. In an example, the strain is generated by stretching an underlying elastomeric substrate to which at least a portion of the interconnect is bonded. For certain embodiments, flexible or stretchable interconnects include interconnects having wavy, meandering or serpentine shapes.

“Bending stiffness” is a mechanical property of a material, device or layer describing the resistance of the material, device or layer to an applied bending moment. Generally, bending stiffness is defined as the product of the modulus and area moment of inertia of the material, device or layer. A material having an inhomogeneous bending stiffness may optionally be described in terms of a ““bulk” or “average” bending stiffness for the entire layer of material. A material made up of a plurality of components, e.g., substrate and barrier layers, electrical circuit, may be described in terms of a “net bending stiffness”, which is a compilation and average of each component's bending stiffness.

“Conformal contact” refers to contact established between a device and a receiving surface, which may for example be a target tissue in a biological environment. In one aspect, conformal contact involves a macroscopic adaptation of one or more surfaces (e.g., contact surfaces) of an implantable device to the overall shape of a tissue surface. In another aspect, conformal contact involves a microscopic adaptation of one or more surfaces (e.g., contact surfaces) of an implantable device to a tissue surface resulting in an intimate contact substantially free of voids. In an embodiment, conformal contact involves adaptation of a contact surface(s) of the implantable device to a receiving surface(s) of a tissue such that intimate contact is achieved, for example, wherein less than 20% of the surface area of a contact surface of the implantable device does not physically contact the receiving surface, or optionally less than 10% of a contact surface of the implantable device does not physically contact the receiving surface, or optionally less than 5% of a contact surface of the implantable device does not physically contact the receiving surface. Conformal contact includes large area conformal contact, for example, wherein conformal contact between a tissue and device component is over an area greater than or equal to 1000 mm², and optionally greater than or equal to 10,000 mm². In an aspect, the tissue is brain tissue. Conformal contact may also be described in terms of the maximum separation distance between the device and the underlying brain tissue that the device is interfaced with, such as a distance that is less than or equal to 1 mm. In addition, the tissue may have an intervening thin film of brain fluid between the brain tissue and the device. Accordingly, physical contact with brain tissue includes physical contact between the device and any biological film, including brain fluid, surrounding the brain, so long as electrical contact between the device and brain is maintained.

Devices and methods provided herein are useful for “monitoring” or “actuating” electrical spatio-temporal waveforms over the brain surface. Monitoring refers to measuring, and optionally encoding, spatio-temporal electrical waveform on the brain surface. Actuating refers to the electrodes of the device interacting with, stimulating, controlling, or otherwise affecting brain tissue, or a material (e.g., skull, skin) or fluid (e.g., cerebral fluid) in electrical contact therewith.

“Temporally adjacent time points” is a measure of the time resolution of the device. The ability to electrically detect changes in electrical potential in a brain location is constrained by how often the electrical measurement is made or the delay time between measurements. The ability to stimulate brain waveform change is similarly constrained.

“Electrical waveform” refers to a pattern of electric potential over the brain surface. A single waveform snapshot provides only limited information about the waveform at one single instance in time. Accordingly, a spatio-temporal profile requires monitoring of the waveform over a period of time. This provides information about the direction of travel of the waveform, how it initiates, propagates and terminates. That information is required to further identify a waveform as “abnormal” or “normal”.

The methods and devices provided herein are particularly useful in detecting various types of waveforms, including waveforms that are associated with abnormal brain states such as during a seizure. A “reentrant spiral loop” refers to a pathway that can generate a sustained spiral wave when stimulated. A “spindle oscillation” is associated with a sleep state and having a waveform that is highly synchronous (“temporally coherent”). Identifying waveforms can provide classification as to whether the patient is in an “interictal” (between seizure/convulsions) or an “ictal” (seizure/convulsions) state.

“Substantially planar” refers to a waveform having a preferential propagation direction that is linear in nature, and includes a plane wave where electrical potential increases or spikes travels geographically from one side of the device toward another side. In contrast, a “spiral loop” refers to a waveform that is non-linear in nature in that the electrical potential spikes spiral about a central point, at least for a certain time period. A “traveling wave” is used to refer to a pattern of electric potential whose position depends on time and, accordingly, may include planar waves, substantially planar waves, spiral loops, and combinations thereof such as a planar wave that transitions to a spiral loop for a certain time period that may either dissipate or transition back to a substantially planar wave geometry. “Preferential propagation direction” Algorithms may be employed to classify a “preferential propagation direction” such as by calculating the position of a weighted potential average over the entire monitored brain surface with time. For situations where the resultant position plot is generally linear, such as by calculating a regression or correlation coefficient that is within a user-specified range, the waveform may be classified as substantially planar or planar. Similarly, for situations where there is a spiral relation, the waveform may be classified, at least in part, as a spiral waveform. Further refinement, such as separately classifying waveforms that are geographically distinct, provides capability of classifying multiple waveforms at any given timepoint. Current devices and methods having large footprints and fine spatial and temporal resolution are particularly compatible with this aspect.

“Principal components analysis” is a mathematical procedure known in the art to transform the number of original variables into principal components by an orthogonal linear transformation and is considered a form of eigenvector-based multivariate analyses. Other methodologies known in the art may be used in the analyzing step, including for example, pattern recognition (where the detected waveform is compared against known waveforms), clustering algorithms, and machine learning.

“Spatial resolution” refers to the ability to spatially resolve electric potential between brain locations, and is dependent on the electrode spacing in the device as the device is in conformal contact with the brain tissue. The spatial resolution may relate to monitoring a waveform or actuating a waveform.

“Temporal resolution” refers to the sampling rate of the device, or the time between samples. The sampling rate determines the highest resolvable frequency content of the sampled signal through the Nyquist-Shannon sampling theorem.

“Conformable contact area footprint” refers to the area over which interfacing takes place between the conformable device and the brain tissue. This footprint generally corresponds to the area defined by the outermost electrodes in the electrode array, or at least for those electrodes in electrical contact with the brain surface.

“Controller” refers to the portion of the device that controls electrode energization for waveform actuation and collection of data related to monitoring of spatio-temporal electrical waveform profile over the brain surface. In addition, the controller may encompass circuitry used to actuate therapeutic devices that are operably connected to the conformable device.

FIG. 1 illustrates one embodiment of the conformable device 10 comprising a conformable substrate 100 that supports a conformable electronic circuit 110. FIG. 1A is a top view, with four unit cells shown and with the dots indicating that any number of unit cells may be incorporated into the circuit. The circuit 110 comprises a deformable array of electrodes 120 in electrical communication with a plurality of deformable interconnects 130. In the embodiment illustrated in FIG. 1A, the electrodes 120 are connected via interconnects 130 to a pair of transistors 160 (multiplexing transistor) 170 (buffer transistor) in series. Interconnects also provide one row select line 132 per row of the electrode array (shown as the horizontal lines running below the unit cells) which enables the electrodes in that particular row to drive the shared output line 131 (shown as vertical lines to the left of the unit cell) when that particular row is selected. Subsequently, a different row is selected and all of the others de-selected. In this manner, all of the electrodes on the array can be sampled sequentially. The multiplex output from the circuit 110 is provided to controller 150. FIG. 1A shows separate controllers connected to each of interconnect lines 131. In an aspect, the lines 131 are connected to a single controller 150. Controller 150 may comprise an adjustable current source, current mirror, band-pass filters and operational amplifiers, as summarized in FIG. 23. Passage 210 is formed through the device 10, including substrate 100 to provide access to brain tissue underlying conformable device 10, as explained further in FIG. 1B.

FIG. 1B is a side view of a device 10 having a thickness 200. Electrodes 120 are supported by barrier layer 140, such as a top surface 142 of barrier layer 140, which in turn is supported by substrate 100. The electrodes are optionally coated with an electrode coating layer 122. In this embodiment, controller 150 is in operable communication or connection 310 with a therapeutic device 300. Passage 210 is configured to receive at least a portion of therapeutic device 300. Therapeutic device 300 is any device capable of delivering a therapy to brain tissue, such as delivery vessel for a biological or chemical agent (e.g., microsyringe), a penetrating electrode, or an optical fiber for delivering electromagnetic radiation.

FIG. 1C is another embodiment of a device for spatio-temporally interfacing with brain tissue. The device in FIG. 1C is configured so that one-half of the electrode array is used to monitor electrical activity and the other half of the electrode array for electrical stimulation of the brain. The general design is based on the one illustrated in FIG. 1A, but with one transistor removed from unit cells in alternating columns. Removing this transistor enables that column to be used as a multiplexed stimulating electrode. Therefore, half of the electrode array is dedicated to recording only (columns OUT0 . . . OUTN) and half of the electrode array is dedicated to stimulation (columns STIM0 . . . STIMN).

In this example, the device array comprises ten columns for recording, ten columns for stimulation, with eighteen total rows. This yields 360 total electrodes: 180 recording electrodes and 180 stimulating electrodes.

The stimulation is provided from off of the array using a controller, similar to 150, except that it is selectively connected to a positive or negative voltage or current source at a specified time to enable stimulation. Two switches per column (off of the array, but shown on the attached diagram, or as a part of controller 150) are used for this function. If both switches are left open, the device does not stimulate. To stimulate, one of the switches is closed at the same time the desired row or rows are selected. When both switches are closed, the selected rows and columns are stimulated. To balance the overall charge delivered, a second stimulation pulse can be subsequently delivered with opposite polarity. Balancing the delivered charge prevents electrode damage.

The stimulation delivered in the device illustrated in FIG. 1C is shown as constant voltage (as indicated by the labels +V and −V), but by substituting constant current sources in the place of the +V and −V connections, the stimulation can be constant current stimulation.

FIG. 2D shows the device 10 folded over a support material 510, having a first surface 512 and a second surface 514. In this configuration, a first portion 513 of the electrodes is supported by first surface 512 and a second portion 515 of the electrodes is supported by first surface 514. In this configuration, the folded device 10 is inserted into an invagination so that electrodes 513 interface with one surface of the invagination and electrodes 515 interface with a second surface of the invagination opposed to the first surface.

FIG. 38 summarizes additional applications of high density neural (HDN) arrays. In the top panel, a passage 210 (see FIG. 1), in a central portion of the device provides access to a therapeutic device. For example, a sharp external stimulation device such as a penetrating electrode. The penetrating electrode may be short for action potential mapping or shallow electrical stimulation, or long for deep brain mapping or electrical stimulation. A micropositioner may also be electronically controlled to provide precise penetration depth. Another example is a micro-syringe for surface or penetrating injection of a chemical or biologic, such as injection of various medicaments onto the brain surface, in shallow penetration or deep into brain. The therapeutic device may be an optical fiber to provide optical stimulation, such as optical stimulation for genetically modified neuron, wherein the optical source may be a laser, such as a commercially-available laser diode. The bottom panel is a large area HDN array (32×32), for interfacing with larger areas of neural tissue. This system is conducive for conducting a range of experiments, such as measuring sleep spindles, visual stimulation, epilepsy mapping, task related potential mapping. The larger area coverage is suitable for sensor and/or motor cortex evaluation and can be used for larger brain sizes (e.g., monkey, pig, human).

Example 1 Flexible, Foldable, Actively Multiplexed, High-Density Surface Electrode Array for Mapping Brain Activity In Vivo with Single Trial Resolution

Arrays of electrodes for recording and stimulating the brain are used throughout clinical medicine and basic neuroscience research, yet are unable to sample large areas of the brain while maintaining high spatial resolution because of the need to individually wire each passive sensor at the electrode-tissue interface. To overcome this constraint, we have developed new devices integrating ultrathin and flexible silicon nanomembrane transistors into the electrode array, enabling new dense arrays of thousands of amplified and multiplexed sensors connected using many fewer wires. We use this system to record novel spatial properties of brain activity in vivo, including sleep spindles, single-trial visual evoked responses, and electrographic seizures. Our electrode array allowed us to discover that seizures are manifest as recurrent spiral waves which propagate in the neocortex. The developments reported here herald a new generation of diagnostic and therapeutic brain-machine interface (BMI) devices.

The conventional electrode arrays in use today can either sample broad regions of the brain (˜80 mm×˜80 mm) at low spatial resolution (˜10 mm spacing), or small regions of brain (˜4 mm×˜4 mm) at high spatial resolution (˜400 μm spacing)¹, with both requiring N wires for N electrodes. Here we show a 360-channel active electrode array capable of sampling a 5-fold larger region of brain (10 mm×9 mm) than prior work¹, with high spatial resolution (500 μm spacing) and high temporal resolution (>10 kS/s) while reducing the number of wires 9-fold. This technology offers the spatial resolution of voltage sensitive dyes, with greatly improved temporal resolution and signal to noise ratio, with the ability to record from non-optically accessible areas and in a potentially fully implantable, non-toxic, clinical-scale system. This technology can be rapidly scaled to clinical sizes (˜80 mm×˜80 mm), enabling elucidation of micro-scale brain dynamics in human normal brain activity and disease.

The utility of high-resolution neural recordings from the cortical surface for basic research and clinical medicine has been shown for a wide range of applications. Spatial spectral analysis of electrocorticograms (ECoG) from the superior temporal gyrus and motor cortex demonstrate that electrode spacing should be 1.25 mm or closer in humans to sufficiently capture the rich spatial information available². Motor control signals³ and spoken words⁴ can be decoded with substantially improved performance utilizing electrodes spaced 1 mm apart or less. In occipital cortex, arrays with 500 μm spacing have demonstrated micro-field evoked potentials that can distinguish ocular dominance columns⁵. The spatial scale for some pathologic signals is also submillimeter, based on observations of microseizures, microdischarges and high frequency oscillations in epileptic brain^(6,7).

Yet the subdural electrodes in use clinically, for example, in the diagnosis and treatment of epilepsy, are much larger (˜3 mm diameter) and have large interspacing (˜10 mm) because of the clinical need to record from large areas of the brain surface (80 mm×80 mm) in order to accurately localize seizure generating brain regions. Large area electrode arrays with high spatial resolution are also needed in BMI applications to account for variability in the location of brain functions, which can vary by ˜5 mm across subjects⁸⁻¹¹. High-resolution interface over a large area has previously been impossible due to the infeasibility of connecting thousands of wires in the small intracranial space.

Much of the existing research in electrode technology has focused on penetrating electrode arrays, such as the Utah array¹, which can provide a high-resolution interface to a small area of cortex and enable high-performance neuromotor prostheses¹². However, arrays of penetrating microelectrodes may only function 6-12 months¹³ before the signal quality on most electrodes is substantially diminished. These devices can also cause hemorrhage and inflammatory tissue responses from the immediate insertion^(14,15) and over long periods of time, possibly due to the inability of the rigid penetrating electrodes to flex and move as the brain pulses, swells and contracts¹⁶.

Highly flexible arrays of subdural electrodes have unique advantages over penetrating microelectrode arrays in that they are able to maintain signal quality over extended periods of time with minimized irritation and injury to brain tissues¹⁷⁻²⁰. Further, the micro-electrocorticographic (μECoG) signal recorded from flexible arrays of non-penetrating electrodes with high-resolution can provide comparable information content to the spiking activity recorded by penetrating microelectrodes in some applications, such as BMI²¹⁻²⁶.

Electrode Array Fabrication and Testing:

To access high-resolution interface with large areas of the brain, we have developed an array of flexible, non-penetrating electrodes using novel flexible silicon electronics technology. The array is composed of 720 silicon nanomembrane transistors (FIG. 2A). The active matrix circuit design contains two transistors per unit-cell (FIG. 2B, left frame and FIG. 1). The buffer transistor 170 connected to the electrode 120 provides buffering of the biological signals, while the multiplexing transistor 160 allows all of the electrodes in the same column to share a single output wire 131. Flexible transistors are fabricated using high-quality single-crystal silicon, yielding a mobility of ˜350 cm²/Vs and an on/off ratio>10³, calculated from the slopes of the transfer curves and the ratio of maximum and minimum current outputs (FIG. 2B, center and right frames) by using standard field-effect transistor models²⁶. This capability enables high speed multiplexing (<5 μs) and sampling rates >10 kS/s per electrode²⁷.

Active electrode arrays are fabricated using a multi-layer process, schematically illustrated in the exploded view in FIG. 2C (see also U.S. Pat. App. No. 12968637 filed Dec. 15, 2010 and PCT App. No. PCT/US10/60425 filed Dec. 15, 2010; which are hereby specifically incorporated by reference). Doped silicon nano-ribbons (˜260 nm) are located in the first layer through the use of transfer printing technology. Subsequent horizontal and vertical metal interconnect layers are insulated using layers of polyimide (PI, ˜1.2 μm, Sigma Aldrich, USA). Additional polymeric encapsulation layers (PI and epoxy, ˜1.2 μm and ˜4 μm) with an offset vertical interconnect access (VIA) structure (FIG. 2C, right panels) prevent electrical leakage currents when the device is immersed in highly conductive bio-fluids. As a final step, platinum (Pt, ˜50 nm) is evaporated and deposited onto the surface electrodes to reduce their impedance (˜20 kOhm at 1 kHz). Detailed fabrication procedures, corresponding microscope images and a cross-sectional schematic can be found in the Methods section and in FIG. 7.

Conventional electrode technology is technically limited in its ability to record from inside of sulci. However, implanting even a few electrodes in sulci such as the central sulcus, has shown that the signals obtained carry more information for BMI applications than signals recorded from the traditional gyral surface²⁸. Electrical recording from inside sulci may also be important for clinical applications, as studies of brain pathology have demonstrated that focal cortical dysplasias are preferentially located at the bottom of sulci²⁹. Some devices have attempted to address this by exposing a small number of passive electrodes on both surfaces of the device^(30,31), but only achieved limited spatial sampling.

The extreme flexibility of our device allows it to be folded around a substrate, such as a silicone rubber substrate that is about 700 μm thick, forming a unique, high-resolution, double-sided recording device that allows access to rarely explored cortical areas, such as the interior of sulci or the medial aspects of the cerebral hemispheres (FIG. 2D). To minimize induced strain in the silicon, silicon dioxide and metal interconnection layers during folding, the overall array thickness has been reduced from our previous efforts, 76 μm²⁷, to 25 μm, resulting in a nearly 10-fold reduction in bending stiffness. This is accomplished by reducing the PI substrate thickness from 25 μm (FIG. 2E, blue trace) to 12.5 μm (FIG. 2E, red trace), and by reducing the epoxy encapsulation thickness from 20 μm to 8 μm (FIG. 2E, arrows). The induced strain in each layer during folding is estimated via analytical modeling (FIG. 2F) and is maintained well below the mechanical fracture strain of each inorganic material (˜1% for Si and SiO₂ ³²).

In Vivo Experiments:

We use our flexible electrode device to map neural activity at high resolution, on the surface of visual cortex of 10 cats in vivo (FIG. 3A). An initial craniotomy and durotomy exposed a 2×3 cm region of cortex. Eyes were focused on a monitor that subtended 28°×22° of space. The electrode arrays are either placed on the brain (FIG. 3A) or inserted into the interhemispheric fissure, as shown in the inset of FIG. 3A and FIG. 3B, right frame. Given the high flexibility of the electrode array, it can be placed in between the two hemispheres of the brain without causing damage to tissue. In this configuration, the recording surface is facing the left hemisphere. Alternately, the folded electrode array can be inserted in the same location as the flat electrode array (FIG. 3B, left frame), simultaneously recording from both hemispheres, with the right hemisphere filtered through the dura. Accordingly, an aspect of the invention relates to simultaneous monitoring of both hemispheres of a brain with one device.

Sleep Spindles:

Spontaneous spindles during barbiturate anesthesia are recorded in the μECoG signal. Spindle oscillations consisted of waves repeating at 5-7 Hz, lasting 1-2 seconds and repeating every 6-10 seconds. Due to the large number of channels on the electrode array, and the large number of spindles recorded, data from a representative channel is shown for a typical spindle (FIG. 4A). The signal amplitude of ˜1.2 mV agrees with earlier published reports³³. The unfiltered noise level of 30 μV RMS is greatly improved from our previous report²⁷. Individual waves within spindle oscillations are identified by a detector triggered on a threshold of two standard deviations above or below the mean. For four of these waves, the root-mean-square (RMS) value of the zero-meaned signal in the 30 ms window before and after the peak is plotted on the array map (FIG. 4B, I-IV, left column). For each channel in the array with >50% of the maximum RMS value, the time to the peak of the wave is plotted (FIG. 4B, I-IV, right column). Individual spindle waves are observed to be spatially confined to a small region of brain (<5 mm×5 mm) and did not move. They are highly synchronous, peaking within a few milliseconds in all of the channels involved.

Visual Evoked Responses:

Visual stimuli consisting of full-field drifting gratings are presented for 504 ms at 2 Hz with a spatial frequency of 0.5 cycles per degree. Single-trial visual evoked potentials³⁴ are visible on many channels of the electrode array. A small subset of these potentials is shown, without averaging, to illustrate the quality of the electrode array recordings (see FIG. 8).

A second visual stimulus consists of flashing white boxes at pseudorandom locations within an 8 by 8 grid are presented in order to measure the retinotopic organization of the recorded cortical area. The duration of each flash is 200 ms, followed by a 64 ms blank time. Stimuli are presented 15 times at each location, for a total of 960 stimulus presentations. Responses from the 15 trials are averaged. The response strength for the 64 different stimulus locations is determined for each of the 360 electrode array channels by calculating the RMS value of the zero-meaned signal within the 40 ms to 160 ms window after presentation of the visual stimulus, to capture the majority of the visual evoked potential³⁴. Response strengths are plotted in FIG. 5A as 64 color maps, each showing the response of the entire 360 channel electrode array. Color maps are arranged in the same physical layout as the stimuli are presented in the visual field, i.e. the image map in the upper left hand corner of the figure represents the neural response recorded from all 360 channels to a flashing box presented in the upper left hand corner of the monitor. The color scale is constant over all 64 image maps and is saturated at the 1^(st) and 99^(th) percentile of recorded response strength, respectively, to improve the visual display. The responses indicate that distinct regions of the brain respond to distinct areas of the visual field, as expected. The electrode color map data is oriented such that the bottom left-hand corner of the electrode array is approximately located over Brodmann area 18, the bottom right-hand corner over area 17, the middle region over areas 18 and 19, the upper right-hand corner over area 21 and upper left-hand corner over area 7 (inset, FIG. 5B).

For each channel in the array with a response >50% of the peak RMS value (as calculated above), the delay to the peak of the evoked response is determined (FIG. 5B). Channels below threshold are shown as white. A few general observations are visible in the data. Stimuli presented in the lower and left areas of the screen appear to activate small areas of the lower left-hand corner of the electrode array and these responses occur earliest, consistent with early visual cortical areas³⁵. Stimuli presented in middle to upper-middle areas of the visual field appear to elicit responses in large areas of the upper middle areas of the electrode array and these activations occur later, consistent with visual association cortex. The upper two rows of the screen appear to be outside of the visual field covered by the array.

As a more rigorous test of the ability of the electrode array to resolve the visual field, the evoked response data is used to train a deep belief net (DBN) classifier^(36,37). A training set is generated by randomly selecting 10 out of the 15 trials, averaging the evoked responses and repeating this process 100 times for each of the 64 screen locations to yield 6400 total samples. The evoked response feature vectors are calculated as in FIGS. 5A and 5B, and concatenated, giving 720 feature dimensions in each of the 6400 samples. The trained deep belief net is tested on a separate dataset of 10 trials, averaged together, from the same animal and recording day. The prediction performance is illustrated as image map of the visual field in FIG. 5C. 23 of the 64 screen locations (36%) are predicted exactly correct (black boxes), significantly better than chance (1.6%). 41 of 64 (64%) screen locations are predicted correctly within 1 neighboring square (grey boxes, distance≦√2, chance level 11.8%).

Electrographic Seizures:

As a third demonstration of this new technology, seizures are induced in the feline model using local administration of picrotoxin. The drug is placed directly on the brain, adjacent to the electrode array on the frontal-medial corner. In one of the animal experiments, the electrode array records four spontaneous electrographic seizures and hundreds of interictal spikes over ˜1 hr. The μECoG signal from a single channel of the electrode array during a short electrographic seizure (FIG. 6A) demonstrates large amplitude (6.6 mV), low noise (45 μV RMS) and high signal-to-noise ratio (SNR, 34 dB).

The array recorded spatial patterns never previously observed during seizures. At the ictal onset, there is a plane wave (I) coming from the upper left which encounters a phase anisotropy, bends to the right, and anticipates the subsequent clockwise spiral (wave II). This spiral pattern (wave II) rotates for 3 cycles. A second incoming plane wave (III) changes the direction of rotation of the spiral. The ensuing counterclockwise spiral (wave IV) rotates for 19 cycles and is terminated by a plane wave (V) coming from the right.

Based upon these observations, it is possible that neocortical seizures are initiated by interictal spikes diverted asymmetrically around regional anisotropies, resulting in sustained reentrant loops. Seizures may be terminated by mutual annihilation of a rotating spiral with a traveling wave, which has implications for electrical stimulation to disrupt seizures³⁸. Analogous anisotropies and colliding waves have been observed in the genesis and termination of cardiac arrhythmias³⁹.

Band-pass filtering the spiral wave data to investigate only the primary frequency component (as in previous analysis⁴⁰⁻⁴²) yields delay plots that are consistent with spiral waves. The primary frequency of the counter-clockwise spiral during the seizure is 6 Hz (see FIG. 9). Spiral wave data is band-pass filtered from 4 to 8 Hz using a 6th order butterworth band-pass filter in the forward and reverse directions, resulting in zero-phase distortion digital filtering (and effectively doubling the order of the filter to a 12^(th) order filter). The relative delay for each electrode is calculated by first upsampling by a factor of 12 and then taking the index of the maximum cross-correlation between each channel and the average of all 360 channels. The resulting delay image map (FIG. 6C) shows a singularity as if forming a counterclockwise rotating pinwheel. Clockwise motion is also demonstrated by the delay plot (FIG. 6D) albeit with a less clear singularity.

In addition to the spatiotemporal patterns analyzed above, the large SNR of the electrode array facilitates pattern analysis of single ictal and interictal spikes. Stereotyped, repetitive spatiotemporal patterns of single spikes are frequently observed throughout the dataset. We develop a clustering algorithm to test whether the spatiotemporal patterns of single spikes can be classified consistently. First a standard, threshold-based spike detector is run on the average of all 360 channels to provide event detections. The threshold is set at −500 μV with a refractory period of 160 ms. Data from all 360 electrodes are band-pass filtered from 1 to 50 Hz in the window 60 ms before and 100 ms after the threshold crossing. The data is then upsampled by a factor of 12 and cross-correlated with the average of all 360 channels. The relative delay of the spike on each channel is calculated using the index of the maximum correlation value. In addition, the magnitude of the spike on each channel is calculated using the RMS value of the zero-meaned signal within the same window. The 360-element delay and RMS vectors representing each spike are normalized by dividing by their maximum respective values and concatenated. Using these two features, relative delay and RMS, the speed and direction of the wavefront, as well as its amplitude, is encoded.

To lessen the computational burden before clustering, principal components analysis (PCA) is used to reduce the dimensionality of the spike data from 720 to 81—a number of coefficients that accounts for 99% of the data variance. Finally, k-medoids clustering⁴³ is carried out on 877 detected spikes. The potential number of clusters, k, ranged from 1 (i.e. no clustering) to 30 and the gap statistic⁴⁴ is then used to determine the optimal number of clusters⁴⁵. 21 clusters are returned. Delay maps for all of the spikes in each cluster indicate a strong similarity within clusters (see FIGS. 10 to 20).

Example relative delay image maps for six different clusters are shown to illustrate their differences (FIG. 6E, left frames). The events in the six clusters shown are found both ictally and interictally. The representation of each event on a standard clinical electrode, based on the average signal, is plotted as a trace (FIG. 6E, right). They illustrate that vastly different micro-scale spatial patterns can be indistinguishable when detected with conventional systems on a macro-scale. This data explicitly demonstrates the functional benefit of the present high density, high resolution device arrays for recording a spatio-temporal profile from brain at high spatial resolution.

5 of the 21 clusters appeared to occur only during seizures. One example each from two of these clusters is shown (FIG. 6F). These results suggest that μECoG can differentiate ictal from interictal patterns that would show up as nearly identical spikes at the resolution of clinical EEG.

Discussion:

Spiral activity is described by mathematical models of 2-dimensional excitable media⁴⁶ and is documented in brain and heart,³⁹⁻⁴² but until now a tool did not exist to record exhaustive spatiotemporal patterns of brain activity in a large mammalian brain, as we demonstrate here. Our results not only demonstrate the presence of spiral waves during seizures with unprecedented detail but also, and perhaps more importantly, offer a method to record such waves in a chronic fashion in awake, behaving animals and humans.

The significance of high density, active array technology is evident in the neural dynamics which emerge at a spatial scale 400 times finer than used clinically. This technology demonstrates complex spatial patterns, such as spiral waves, clustering of spatiotemporal patterns, and heterogeneity and anisotropy of sleep oscillations, all of which occur within the space occupied by one current clinical ECoG electrode. Whereas coarse spatial undersampling prevents current technology from resolving the micro-scale spatial patterns that occur in the brain, the high resolution of the active array technology enables us to distinguish intrinsic from pathologic signals efficiently, even within the same frequency bands.

We report that spindles are spatially punctate and temporally coherent, whereas electrographic seizures propagate as planar and spiral waves. Although prior investigations using voltage-sensitive dyes have found spiral waves in rodents during EEG epochs dominated by sleep-like delta frequencies⁴², in contrast, we demonstrate activity which is spatially inhomogeneous and does not spiral, yet are present during delta-dominant states, and which appear as sleep spindles electrographically. While optical imaging has demonstrated spatial patterns such as planar waves and spirals in disinhibited rat cortex⁴¹, high-density, active array technology enables us to show that these spiral dynamics in disinhibited cat cortex are electrographic seizures at the clinical scale.

Ultimately, the question of clinical relevance is whether there are spiral waves in human cortex, yet voltage sensitive dye recordings are infeasible for use in humans due to the requirement that the brain be optically exposed and subjected to toxic dyes. Our results suggest that technology incorporating flexible, high-density, active arrays of electrodes can provide equal or superior recordings in a fully implantable system. If spiral waves are demonstrated in human cortex, the clinical implications are profound. Seizure control may be analogous to the control of cardiac arrhythmias, which are also known to manifest as reentrant spiral waves of excitation³⁹. Further, as learning tasks increase spindle activity⁴⁷, which may be due to consolidation and integration of memories⁴⁸, understanding the fine structure of spindles has implications for learning and memory efficiency, as well as thalamocortical networks involved in sleep and primary generalized epilepsy.

Finally, flexible devices such as those shown here hold the promise to enable neuroprosthetic devices that have been limited until now by the lack of resolution of the brain-machine interface and by the irregular topography of the brain. Utilizing the extreme flexibility of active electrode arrays, devices can be folded and implanted into currently inaccessible brain regions, such as sulci and fissures that can be simultaneously recorded and stimulated, along with surface regions to enable devices to facilitate movement, sensation, vision, hearing and cognition. These devices can also be powered remotely through wireless power transmission techniques⁴⁹.

Our work also has implications for treating disease. Disorders such as epilepsy, dementia, affective disorders, movement disorders and schizophrenia are all conditions that affect dispersed brain networks, rather than a single locus of brain function. Investigations of major depression, parkinsonism, and chronic pain with magnetoencephalography has identified “thalamocortical dysrhythmia,” but increases of spatial and temporal resolution as with the recording method presented here would allow a more detailed characterization of these diseased networks⁵⁰. Only with new approaches that can resolve micro-scale activity over large areas of cortex will we be able to begin to understand how the brain functions in both disease and health, and to develop better diagnostic and therapeutic options for those affected.

Methods. Fabrication of the Active Electrode Array:

Doped silicon nano-ribbons on a silicon-on-insulator (SOI) wafer (Si(260 nm)/SiO₂(1000 nm)/Si; SOITEC, France) are prepared using a high temperature diffusion process (950-1000° C.) in a rapid thermal annealing (RTA) system. These nano-ribbons were transfer printed onto a PI substrate (12.5 μm, Kapton, Dupont, USA) using spin-coated, uncured polyimide (PI) as a glue layer. Once the PI is cured, gate oxide is deposited with plasma enhanced chemical vapor deposition (PECVD, plasmatherm, USA). Contact openings for the source and drain connects are made with a buffered oxide etchant (BOE, Transene, USA). Finally, metal (Cr/Au, 5 nm/150 nm) is deposited using an electron beam evaporator (Temescal, USA), creating n-type transistor arrays. Each unit cell contains 2 transistors, which are connected by metal lines. Subsequent layers of metal interconnections are electrically isolated with polymeric inter-layer dielectric (1.2 μm, polyimide, Sigma Aldrich, USA). Following two metal circuit interconnection layers, a water-proof encapsulation is formed with a photocurable epoxy (SU8, Microchem Corp), protecting the device while submerged in conductive bio-fluids. The completed active sensor array is connected to an interface circuit board through a flexible anisotropic conductive film (ACF, Elform, USA).

Multiplexing:

The connections between four unit cells are illustrated in FIG. 23 (FIG. 1 illustrates any number of unit cells may be employed). When connected to an external constant current sink the selected unit cell completes the current path from +V to −V and forms a source-follower amplifier. The buffered voltage output is from the same circuit node that is connected to the constant current sink. The 18 row select signals of the multiplexed electrode array are cycled at 5 kHz to sample all of the electrodes on the array, yielding a sampling rate of ˜277 Hz per active electrode, with all 18 electrodes in a given column sampled sequentially.

Data Acquisition:

The multiplexed analog signals were synchronously sampled at 100 kHz using a custom data acquisition system (see FIGS. 22-31). 20 times oversampling per switch interval is used to improve the SNR (yielding the 5 kHz cycling rate previously stated). With faster analog to digital converters, the electrode sampling rate can be readily increased to 12.5 kS/s without loss of SNR²⁷. Voltage data are recorded from all 360 electrodes of the active electrode array. The reference (ground) electrode for the acquisition system is clipped to nearby, exposed muscle. Except where otherwise indicated, the μECOG data for all experiments are band-pass filtered from 1 to 50 Hz.

Animal Experiments:

Experiments are conducted in accordance with the ethical guidelines of the National Institutes of Health and with the approval of the Institutional Animal Care and Use Committee of the University of Pennsylvania. Surgical and stimulation methods are as described in detail previously⁵¹,⁵². Briefly, adult cats (2.5-3.5 kg) are anesthetized with intravenous thiopental with a continuous infusion (3-10 mg/kg/hr) and paralyzed with gallamine triethiodide (Flaxedil). Heart rate, blood pressure, end-tidal CO₂ and EEG are monitored throughout the experiment to assure depth and stability of anesthesia and rectal temperature was kept at 37-38° C. with a heating pad. The surface of the visual cortex is exposed with a craniotomy centered at Horsley Clarke posterior 4.0, lateral 2.0.

During visual stimulation, the corneas are protected with contact lenses after dilating the pupils with 1% ophthalmic atropine and retracting the nictitating membranes with phenylephrine (Neosynephrine). Spectacle lenses are chosen by the tapetal reflection technique to optimize the focus of stimuli on the retina. The position of the monitor is adjusted with an x-y-stage so that the area centralae were centered on the screen. Stimuli are presented on an Image Systems (Minnetonka, Minn.) model M09LV monochrome monitor operating at 125 frames per second at a spatial resolution of 1024×786 pixels and a mean luminance of 47 cd/m².

REFERENCES (EXAMPLE 1)

-   1. Campbell, P. K. et al. A silicon-based, three-dimensional neural     interface: manufacturing processes for an intracortical electrode     array. IEEE transactions on bio-medical engineering 38, 758-68     (1991). -   2. Freeman, W. Spatial spectral analysis of human     electrocorticograms including the alpha and gamma bands. Journal of     Neuroscience Methods 95, 111-121 (2000). -   3. Kellis, S. S. et al. Human neocortical electrical activity     recorded on nonpenetrating microwire arrays: applicability for     neuroprostheses. Neurosurgical focus 27, E9 (2009). -   4. Kellis, S. et al. Decoding spoken words using local field     potentials recorded from the cortical surface. Journal of neural     engineering 7, 056007 (2010). -   5. Kitzmiller, J. P. et al. Micro-field evoked potentials recorded     from the porcine sub-dural cortical surface utilizing a     microelectrode array. Journal of neuroscience methods 162, 155-61     (2007). -   6. Schevon, C. a et al. Microphysiology of epileptiform activity in     human neocortex. Journal of clinical neurophysiology: official     publication of the American Electroencephalographic Society 25,     321-30 (2008). -   7. Stead, M. et al. Microseizures and the spatiotemporal scales of     human partial epilepsy. Brain: a journal of neurology 133, 2789-97     (2010). -   8. Amunts, K. et al. Brodmann's areas 17 and 18 brought into     stereotaxic space—where and how variable? NeuroImage 11, 66-84     (2000). -   9. Branco, D. M. et al. Functional variability of the human cortical     motor map: electrical stimulation findings in perirolandic epilepsy     surgery. Journal of clinical neurophysiology: official publication     of the American Electroencephalographic Society 20, 17-25 (2003). -   10. Fox, P. T. et al. Location-probability profiles for the mouth     region of human primary motor-sensory cortex: model and validation.     NeuroImage 13, 196-209 (2001). -   11. Van Essen, D. C., Newsome, W. T. & Maunsell, J. H. The visual     field representation in striate cortex of the macaque monkey:     asymmetries, anisotropies, and individual variability. Vision     research 24, 429-48 (1984). -   12. Hochberg, L. R. et al. Neuronal ensemble control of prosthetic     devices by a human with tetraplegia. Nature 442, 164-71 (2006). -   13. Ryu, S. I. & Shenoy, K. V. Human cortical prostheses: lost in     translation? Neurosurgical focus 27, E5 (2009). -   14. Polikov, V. S., Tresco, P. A. & Reichert, W. M. Response of     brain tissue to chronically implanted neural electrodes. Journal of     neuroscience methods 148, 1-18 (2005). -   15. Schmidt, S., Horch, K. & Normann, R. Biocompatibility of     silicon-based electrode arrays implanted in feline cortical tissue.     Journal of biomedical materials research 27, 1393-9 (1993). -   16. Griffith, R. W. & Humphrey, D. R. Long-term gliosis around     chronically implanted platinum electrodes in the Rhesus macaque     motor cortex. Neuroscience letters 406, 81-6 (2006). -   17. Margalit, E. Visual and electrical evoked response recorded from     subdural electrodes implanted above the visual cortex in normal dogs     under two methods of anesthesia. Journal of Neuroscience Methods     123, 129-137 (2003). -   18. Chao, Z. C., Nagasaka, Y. & Fujii, N. Long-term asynchronous     decoding of arm motion using electrocorticographic signals in     monkeys. Frontiers in neuroengineering 3, 3 (2010). -   19. Yeager, J. D. et al. Characterization of flexible ECoG electrode     arrays for chronic recording in awake rats. Journal of neuroscience     methods 173, 279-85 (2008). -   20. Yu, Z. et al. Monitoring hippocampus electrical activity in     vitro on an elastically deformable microelectrode array. Journal of     neurotrauma 26, 1135-45 (2009). -   21. Andersen, R. a, Musallam, S. & Pesaran, B. Selecting the signals     for a brain-machine interface. Current opinion in neurobiology 14,     720-6 (2004). -   22. Mehring, C. et al. Inference of hand movements from local field     potentials in monkey motor cortex. Nature neuroscience 6, 1253-4     (2003). -   23. Ball, T. et al. Towards an implantable brain-machine interface     based on epicortical field potentials. Biomed. Tech. (Berlin) 38,     756-759 (2004). -   24. Wilson, J. A. et al. ECoG factors underlying multimodal control     of a brain-computer interface. IEEE transactions on neural systems     and rehabilitation engineering: a publication of the IEEE     Engineering in Medicine and Biology Society 14, 246-50 (2006). -   25. Brunner, P. et al. Rapid Communication with a “P300” Matrix     Speller Using Electrocorticographic Signals (ECoG). Frontiers in     neuroscience 5, 5 (2011). -   26. Streetman, B. G. & Banerjee, S. K. Solid State Electronic     Devices. (Pearson: 1981). -   27. Viventi, J. et al. A Conformal, Bio-Interfaced Class of Silicon     Electronics for Mapping Cardiac Electrophysiology. Science     Translational Medicine 2, 24ra22-24ra22 (2010). -   28. Yanagisawa, T. et al. Neural decoding using gyral and     intrasulcal electrocorticograms. NeuroImage 45, 1099-106 (2009). -   29. Besson, P. et al. Small focal cortical dysplasia lesions are     located at the bottom of a deep sulcus. Brain: a journal of     neurology 131, 3246-55 (2008). -   30. Stieglitz, T. Flexible biomedical microdevices with double-sided     electrode arrangements for neural applications. Sensors and     Actuators A: Physical 90, 203-211 (2001). -   31. Stieglitz, T. Flexible BIOMEMS with electrode arrangements on     front and back side as key component in neural prostheses and     biohybrid systems. Sensors and Actuators B: Chemical 83, 8-14     (2002). -   32. Thompson, S. E. et al. A 90-nm Logic Technology Featuring     Strained-Silicon. IEEE Transactions on Electron Devices 51,     1790-1797 (2004). -   33. Kim, D.-H. et al. Dissolvable films of silk fibroin for     ultrathin conformal bio-integrated electronics. Nature materials     (2010).doi:10.1038/nmat2745 -   34. Padnick, L. B. & Linsenmeier, R. a Properties of the flash     visual evoked potential recorded in the cat primary visual cortex.     Vision research 39, 2833-40 (1999). -   35. Tusa, R. J., Rosenquist, A. C. & Palmer, L. a Retinotopic     organization of areas 18 and 19 in the cat. The Journal of     comparative neurology 185, 657-78 (1979). -   36. Hinton, G. E. & Salakhutdinov, R. R. Reducing the dimensionality     of data with neural networks. Science (New York, N.Y.) 313, 504-7     (2006). -   37. Larochelle, H. et al. An empirical evaluation of deep     architectures on problems with many factors of variation.     Proceedings of the 24th international conference on Machine     learning—ICML '07 473-480 (2007).doi:10.1145/1273496.1273556 -   38. Anderson, W. S. et al. Studies of stimulus parameters for     seizure disruption using neural network simulations. Biological     cybernetics 97, 173-94 (2007). -   39. Witkowski, F. X. et al. Spatiotemporal evolution of ventricular     fibrillation. Nature 392, 78-82 (1998). -   40. Prechtl, J. C. et al. Visual stimuli induce waves of electrical     activity in turtle cortex. Proceedings of the National Academy of     Sciences of the United States of America 94, 7621-6 (1997). -   41. Huang, X. et al. Spiral waves in disinhibited mammalian     neocortex. The Journal of neuroscience: the official journal of the     Society for Neuroscience 24, 9897-902 (2004). -   42. Huang, X. et al. Spiral Wave Dynamics in Neocortex. Neuron 68,     978-990 (2010). -   43. Hastie, T., Tibshirani, R. & Friedman, J. The Elements of     Statistical Learning. (Springer-Verlag: New York, N.Y., USA, 2001). -   44. Tibshirani, R., Walther, G. & Hastie, T. Estimating the number     of clusters in a data set via the gap statistic. Journal of the     Royal Statistical Society: Series B (Statistical Methodology) 63,     411-423 (2001). -   45. Blanco, J. a et al. Unsupervised Classification of     High-Frequency Oscillations in Human Neocortical Epilepsy and     Control Patients. Journal of neurophysiology     (2010).doi:10.1152/jn.01082.2009 -   46. Paullet, J. E. & Ermentrout, G. B. Stable Rotating Waves in     Two-Dimensional Discrete Active Media. SIAM Journal on Applied     Mathematics 54, 1720 (1994). -   47. Gais, S. et al. Learning-dependent increases in sleep spindle     density. The Journal of Neuroscience 22, 6830-4 (2002). -   48. Tamminen, J. et al. Sleep Spindle Activity is Associated with     the Integration of New Memories and Existing Knowledge. Journal of     Neuroscience 30, 14356-14360 (2010). -   49. Sekitani, T. et al. A large-area wireless power-transmission     sheet using printed organic transistors and plastic MEMS switches.     Nature materials 6, 413-7 (2007). -   50. Llinás, R. R. et al. Thalamocortical dysrhythmia: A neurological     and neuropsychiatric syndrome characterized by     magnetoencephalography. Proceedings of the National Academy of     Sciences of the United States of America 96, 15222-7 (1999). -   51. Cardin, J. A., Palmer, L. A. & Contreras, D. Stimulus feature     selectivity in excitatory and inhibitory neurons in primary visual     cortex. Journal of Neuroscience 27, 10333 (2007). -   52. Cardin, J. a, Palmer, L. a & Contreras, D. Cellular mechanisms     underlying stimulus-dependent gain modulation in primary visual     cortex neurons in vivo. Neuron 59, 150-60 (2008).

Bending Stiffness and Bending Strain:

The cross section of the electrode array is shown in FIG. 7B. Since the Au (500 nm and 150 nm), Si (260 nm) and SiO₂ (100 nm) layers between the top SU8 and bottom Kapton® are very thin, and are very close to the neutral mechanical plane, their contribution to the bending stiffness can be approximated by polyimide (PI) within 1% error. Therefore the complex multilayer electrode can be approximated by a two-layer structure, composed of PI of thickness h₂ and SU8 of thickness h₁. The distance of neutral mechanical plane from the Kapton bottom is y₀, and is obtained as

$\begin{matrix} {y_{0} = {\frac{{1{\overset{\_}{E}}_{PI}h_{2}^{2}} + {{\overset{\_}{E}}_{{SU}\; 8}{h_{1}\left( {{2h_{2}} + h_{1}} \right)}}}{{2{\overset{\_}{E}}_{PI}h_{2}} + {{\overset{\_}{E}}_{{SU}\; 8}h_{1}}}.}} & \left( {S\; 1} \right) \end{matrix}$

where

${\overset{\_}{E}}_{i} = \frac{E_{i}}{1 - v_{i}^{2}}$

relates to Young's modulus E_(i) and Poisson's ratio v, (i=PI for PI layer, i=SU8 for SU8 layer).

The bending stiffness of the electrode is

${{EI} = {{{\overset{\_}{E}}_{PI}{{bh}_{2}\left( {{\frac{1}{3}h_{2}^{2}} - {h_{2}y_{0}} + y_{0}^{2}} \right)}} + {{\overset{\_}{E}}_{{SU}\; 8}{{bh}_{i}\left\lbrack {{\frac{1}{3}h_{1}^{2}} + {h_{1}\left( {h_{2} - y_{0}} \right)} + \left( {h_{2} - y_{0}} \right)^{2}} \right\rbrack}}}},$

where b=10 mm is the electrode width.

For the electrode bent to a radius of R, the strain at position of distance y from the Kapton bottom is

$\begin{matrix} {ɛ = {\frac{y - y_{0}}{R}.}} & \left( {S\; 3} \right) \end{matrix}$

Mechanics of Electrode Insertion

The electrode is folded around a soft PDMS of Young's modulus E_(PDMS)=100 kPa, width B and thickness h_(PDMS), and is inserted into the gap of thickness h_(gap) between the hemispheres of the brain. The insertion depth is d (see FIG. 32A). Since the thickness of brain (>10 mm) is much larger than that of the folded electrode (˜0.7 mm), the two hemispheres of the brain are modeled as semi-infinite solids, of Young's modulus E_(brain)=3 kPa and Poisson's ratio v_(brain)=0.35¹. For a semi-infinite solid subject to uniform pressure p at the top surface in the circular region of radius a=√{square root over (Bd)}, the surface subsidence w at position of distance r from the circular center is w=[4(1−v_(brain) ²)p√{square root over (Bd)}]/(πE_(brain))∫₀ ^(π/2)√{square root over (1−r² sin² φ/(Bd))}dφ, whose average over the pressure region is obtained as

$\begin{matrix} {\overset{\_}{w} = {\frac{16\left( {1 - v_{brain}^{2}} \right)p\sqrt{Bd}}{3\pi \; E_{brain}}.}} & ({S4}) \end{matrix}$

Since the electrode array is much stiffer and thinner than PDMS, its deformation due to insertion is negligible compared to PDMS. Therefore, displacement continuity gives the relation between the pressure at the interface p and the average subsidence w of the brain surface due to electrode insertion to be

${{{\frac{p}{E_{PDMS}}h_{PDMS}} + {2\; \overset{\_}{w}}} = {h_{PDMS} - h_{gap}}},$

which gives

$\begin{matrix} {p = {\frac{h_{PDMS} - h_{gap}}{\frac{h_{PDMS}}{E_{PDMS}} + \frac{32\left( {1 - v_{brain}^{2}} \right)\sqrt{{Bd}\text{/}\pi}}{3\pi \; E_{brain}}}.}} & ({S3}) \end{matrix}$

And the maximum compressive strain in the brain due to electrode insertion is obtained as

$\begin{matrix} \begin{matrix} {ɛ^{\max} = {\frac{{- \left( {1 + v_{brain}} \right)}p}{E_{brain}}\left\lbrack {1 - {2v_{brain}} + {2\left( \frac{2v_{brain}}{3} \right)^{\frac{3}{2}}}} \right.}} \\ {= \frac{{- {\left( {1 + v_{brain}} \right)\left\lbrack {1 - {2v_{brain}} + {2\left( \frac{2v_{brain}}{3} \right)^{\frac{3}{2}}}} \right\rbrack}}\left( {h_{PDMS} - h_{gap}} \right)}{\left\lbrack {{\frac{E_{brain}}{E_{PDMS}}h_{PDMS}} + \frac{32\left( {1 - v_{brain}^{2}} \right)\sqrt{Bd}}{3\pi^{3/2}}} \right\rbrack}} \end{matrix} & ({S6}) \end{matrix}$

As the folded electrode inserts into the brain gap (insertion depth d), the gap h_(gap) between the left and right brains decreases and approaches an asymptote h _(gap) (i.e., minimal gap distance). The brain gap h_(gap) can be generally written as an exponentially decaying function of the rubber insertion length d, h_(gap)= h _(gap)+(h_(PDMS)− h _(gap))exp(−d/1 mm), which defines zero insertion length as rubber first touches the brain (i.e., h_(gap)=h_(PDMS)). For h_(PDMS)=700 μm and B=16.2 mm in experiment, the maximum compressive strain in the brain versus the insertion depth of rubber is shown in FIG. 32B for minimal brain gaps h _(gap)=500 and 600 μm.

Circuit Design:

The dimensions of the two transistors in the unit-cell (FIG. 2B, left frame) are equal so that they will have matched performance. The width of both transistors is selected to be as large as possible at 200 μm while still leaving room for large interconnections between adjacent unit cells. The L is selected at 20 μm to be conservative for the processing technology. The resulting W/L ratio of 10 yielded reasonable levels of current output. The width of all metal lines and size of all VIAs was increased by 2˜4× from prior designs to improve reliability and ease manufacturing. The electrode spacing is set at 500 μm based on prior work and to match well with the 500 μm spacing of the ACF ribbon cable, enabling a simple layout of the interconnections.

Current Sinks:

The ideal current sinks shown in FIG. 21 can be implemented using commercially available semiconductors as shown in the block diagram in FIG. 23. This basic circuit that is implemented 20 times, one for each column of the electrode array. The circuit makes use of several commercial semiconductors. The first of which is the LM334 which is a 3-Terminal Adjustable Current Source (National Semiconductor). It is used to set the constant current for the source follower. The current is adjusted via a potentiometer on the third pin (not shown). The constant current generated by the LM334 is mirrored by the current mirror section of the REF200—Current Reference (Texas Instruments). The REF200 is used because the LM334 cannot respond to fast transients while supplying low amounts of current. The REF200 current mirror bandwidth is 5 MHz, to enable fast multiplexing. The REF200 also contains two 100 μA constant current sources, which are not used.

The output of a single column from the electrode array is connected to the current mirror and the non-inverting input of a TLC2274 Op Amp as shown. The TLC2274 Op Amp (Texas Instruments) is used to provide buffering for the output of the source follower amplifier. This op-amp buffers the signal before the high pass filter. Additionally, adding this op-amp allows the buffering to occur as close as possible to the electrode array, minimizing parasitic capacitance and maximizing the switching speed.

The output of the Op Amp is connected to a 0.01 Hz high-pass filter. This very low frequency high pass filter is used to remove the average DC offset introduced by the source follower configuration of the amplifiers on the electrode array. The high pass filter frequency must be very low because it introduces aliasing for signals up to ˜0.1 Hz. Signals lower than this present on one multiplexed channel will be aliased onto the other channels. However, any aliasing that occurs can be removed by a subsequent 1 Hz digital high pass filter on the acquired data.

Data Acquisition System:

The output of the high pass filter is connected using a short cable (2′) to a custom data acquisition system interface (see FIGS. 22B, c and FIGS. 24-31). The signal is buffered again by another TLC2274 op-amp to drive the 15′ cable from the acquisition system interface board to a set of four PXI-6289 data acquisition cards (National Instruments, USA). This second stage of buffering prevents any loading introduced by the long cable run from influencing the high pass filter stage. The data acquisition system, in an aspect, is considered a component of a controller 150.

Gain:

The gain of the electrode array is measured by submerging the array in conductive saline. A second electrode is submerged in the saline approximately 1″ from the electrode array. The second electrode was connected to a 100 mV peak to peak sine wave at 3.14 Hz. This test presents a uniform signal for all of the electrodes on the array to measure. The recording duration of this test is 80 seconds and the sampling rate is ˜277 Hz per electrode. The median signal level for all 360 channels on the array is 68 mV peak to peak, yielding a median gain of 0.68. The spatial distribution of the peak to peak amplitudes measured is shown in FIG. 33 illustrating the uniformity of the gain across the electrode array. ˜83% of the electrode channels are operational for this sample. Channels determined to be not operational through this test are interpolated from neighboring operational channels prior to all of the analyses presented in the main text.

Power Supplies:

This electrode array design does not require symmetric power supplies. That is, the +V supply does not need to be equal and opposite of the −V supply. In fact, the source follower amplifier only requires that the +V supply is greater than V_(in), the input voltage minus V_(t), the threshold voltage of the amplifying transistor. Given that V_(in) is typically within the range +/−100 mV for neural signals, as long as V_(t) is greater than +100 mV (typical values are ˜0.7 V, FIG. 2B, center frame), +V can be directly connected to ground (0V). However, if V_(t) of the array is small or negative, the +V supply can be connected to a separate, small positive voltage supply, such as 0.5V.

Directly connecting the array +V connection to GND or at least minimizing the voltage of the positive supply has several advantages. The most important of which is that it reduces the potential for leakage currents through the gate of the buffer transistor (FIG. 1 b, left frame) by reducing the voltage potential between the electrode (gate) and the silicon substrate. Another advantage is simplifying the power connections for the electrode array.

With the +V of the electrode array de-coupled from the +V of the acquisition system, the acquisition system power supplies can be increased without significantly increasing the potential for leakage current. The power supplies have been increased from +/−2.5V in prior experiments to +/−3.5V in the current experiments. This allows V_(ds) of the buffer transistor (FIG. 2B, left frame) to increase, if needed, to allow the transistor to enter saturation and function properly in the source-follower amplifier. Increasing the acquisition system power supplies also improves the transient response of the buffer op-amps, which allows faster multiplexing.

Additionally, the row select signals are still generated by the acquisition system, so the high level logic (enabled) signal is approximately +V instead of 0V, and the low level logic (disabled) signal is approximately −V. This allows the V_(gs) of the multiplexing transistor (FIG. 2B, left frame), to remain large, improving the conduction of the multiplexing transistor and reducing V_(ds) when turned on.

Operation on Batteries:

Since the power supply rejection ratio (PSRR) of our system is very low, an ultra-low noise power supply is needed to reduce the noise of the ECoG recordings as much as possible. The PSRR is a measure of the ability of an amplifier to reject noise from its power supplies. It is a ratio of the amplitude of a noise signal presented on the power supplies of the amplifier, to the amplitude of that signal measured on the output of the amplifier.

Nickel-metal hydride batteries (NiMH) are chosen to replace the power supplies that power the acquisition system interface board and the electrode array. Batteries can provide a nearly noiseless power source and NiMH batteries in particular can provide very low output impedance, due to their low internal resistance. Since the power consumption of the electrode interface board is low (˜35 mA) and the active electrode power consumption is very low (0.2-0.4 mA), simple AA rechargeable batteries were chosen. The AA batteries chosen had a charge capacity of 2650 mAh, which enabled the system to run for over 3 days on a charge, which is longer than our experiment duration.

The batteries are used in 4-cell and 6-cell configurations directly connected to the acquisition system interface board without any voltage regulation to keep the noise as low as possible. The cell voltage for the NiMH batteries that are used remained fairly constant between 1.2V and 1.3V through most of the discharge cycle. Therefore, the 4-cell configuration can be used to supply the data acquisition interface board with +/−2.4-2.6 volts, while the 6-cell configuration can be used to supply +/−3.6-3.9 volts. The 6-cell configuration is preferred because it increases the performance of the electrode array.

Switching Noise:

Another large contribution to the noise in the electrode output is caused by the switching of the row select signals. Since the row select signals have a large amplitude (5V-7V) and fast rise and fall time (2 μS), they can easily corrupt nearby low-noise measurements through capacitive or magnetic coupling. This noise is not easily avoidable because of the nature of multiplexed sampling. The row select transistors require a large change in voltage to fully turn them on and off. However, this noise can be reduced by discarding the samples from the analog to digital converter that immediately precede and follow the transition on the row select signals. This dictates a minimum over-sampling ratio (the ratio between the analog sampling rate and the multiplexing rate) of 3. If an over-sampling ratio (OSR) greater than 3 is used, the samples that remain after discarding the samples adjacent to the row select signal transition can be averaged, further reducing the recorded noise.

Electrode Materials:

The passive and active electrode devices shown previously utilized gold as the surface electrode material. In the current device, the electrode base metal is still gold, but an additional coating of flat platinum has been added to reduce the electrode impedance. Passive electrodes sized 250 μm×250 μm using the same dimensions and materials processing steps are fabricated to measure the impedance difference. Passive electrodes must be used to measure the impedance because it is not currently possible to measure electrode impedance while integrated in the active electrode.

The mean impedance of the 250 μm×250 μm passive electrodes is 84 kOhm±17% at 1 kHz, while the impedance of the same electrode design coated with flat platinum is 29 kOhm±9% at 1 kHz. Measurements were conducted with the array immersed in normal saline (0.9%). The reduced impedance provided by the platinum coating should increase the current output of the electrode and enable better signal transfer.

The electrodes in the active electrode array illustrated in FIG. 2A are 300 μm×300 μm. Based on linear extrapolation from prior measurements, we expect the impedance of these electrodes to be ˜69% of the measured value (29 kOhm) of the 250 μm×250 μm electrodes or ˜20 kOhm at 1 kHz.

SUPPLEMENTARY REFERENCES

-   1. Taylor, Z. & Miller, K. Reassessment of brain elasticity for     analysis of biomechanisms of hydrocephalus. Journal of biomechanics     37, 1263-9 (2004).

Example 2 Millimeter-Scale Epileptiform Spike Patterns and their Relationship to Seizures

Advances in neural electrode technology are enabling brain recordings with increasingly fine spatial and temporal resolution. We explore spatio-temporal (ST) patterns of local field potential spikes using a new high-density active electrode array with 500 μm resolution. We record subdural micro-electrocorticographic (μECoG) signals in vivo from a feline model of acute neocortical epileptiform spikes and seizures induced with local administration of the GABA antagonist, picrotoxin. We employ a clustering algorithm to separate 2-dimensional (2-D) spike patterns to isolate distinct classes of spikes unique to the interictal and ictal states. Our findings indicate that the 2-D patterns can be used to distinguish seizures from non-seizure state. We find two statistically significant ST patterns that uniquely characterize ictal epochs. We conclude that millimeter-scale ST spike dynamics contain useful information about ictal state. This finding may be important to understanding mechanisms underlying local circuit activity during seizure generation. Such information about spatio-temporal profiles permits investigation of seizure dynamics and their underlying mechanisms and inform new electrical stimulation protocols for seizure termination.

Introduction:

In epilepsy research, many different brain recording techniques have been employed to understand neural dynamics between, prior to and during seizures. Electrophysiological studies employ techniques that range in size and scale from the Utah array [1], which records multi-unit activity from penetrating electrodes 400 μm apart, to scalp Electroencephalography (EEG), which records electrical potentials that are distorted by the skull, scalp, cerebrospinal fluid (CSF) and soft tissues, from electrodes many centimeters apart. Other studies utilize modalities ranging from Magnetoencephalography (MEG) and functional Magnetic Resonance Imaging (fMRI) to Voltage Sensitive Dyes (VSDs). MEG is a non-invasive technique that records magnetic components of potentials perpendicular to the EEG, and suffers from low spatial resolution, similar to EEG. Image-based technologies such as fMRI and VSDs complement standard electrophysiology, though each has its own spatial and temporal resolution limitations. In this study, we use a new, high-spatial density subdural surface active electrode array of 360 channels covering an area of 10 mm×9 mm to measure local field potential (LFP)-scale electrical signals in vivo from an acute feline epilepsy model. We examine spatio-temporal (ST) patterns of LFP spike activity recorded on a millimeter-scale that are unique to seizures.

Methods. Animal Recording:

We analyze micro-electrocorticographic (μECoG) data from an acute in vivo feline model of epilepsy. Adult cats are anesthetized with a continuous infusion (3-10 mg/kg/hr) of intravenous thiopental. A craniotomy and durotomy were performed to expose a 2×3 cm region of cortex. The high resolution electrode array is then placed on the surface of the brain over primary visual cortex, localized by electrophysiological recordings of visual evoked potentials. Picrotoxin, a GABA-A receptor antagonist that blocks inhibition, is topically applied adjacent to the anterior-medial corner of the electrode array in an amount sufficient to induce abnormal electrical spikes and seizures from the covered region [2].

The active electrode array placed on the cortex is used to record data from 360 independent channels arranged in 20 columns and 18 rows, spaced 500 μm apart. Each electrode contact comprises a 300 μm×300 μm square of platinum. Two high-performance, flexible silicon transistors for each electrode buffered and multiplexed the recorded signals[3]. The total array size is 10 mm×9 mm. Signals are recorded with an effective sampling rate of 277.7 Hz per channel. FIG. 34 is a photograph of the array placed on the surface of the visual cortex of a cat.

We analyze 13 minutes and 40 seconds of data for this study, containing 724 automatically detected spikes and 2 seizures, verified by expert review.

Pre-Processing:

All recordings are first band pass filtered between 1 and 50 Hz using a 6th-order butterworth filter in the forward and reverse direction, using Matlab's filtfilt function, to achieve zero-phase filtering.

64 of 360 channels are non-functional due to manufacturing imperfections. The missing data for these channels are interpolated from the surrounding electrodes using a 2-D averaging spatial filter of window size 3×3 pixels.

Small offsets in time that result from row multiplexing are corrected by upsampling and shifting the data in order to accurately align rows in time. Data are first upsampled by the number of rows within the array (18) via Matlab's interp function, which applies a low pass filter interpolation algorithm. Data are then temporally shifted by their respective row offset.

Spike Detection:

We use a voltage-threshold detector to detect spikes on the signal obtained by averaging all 360 channels. The voltage threshold is set by visual inspection at about 500 μV. When the average signal crossed the threshold from above, a 160 msec segment of the recording is stored (60 msec prior to the crossing and 100 msec post-crossing).

We analyze only single spikes (i.e. no poly-spikes) and retain only spikes which occur on a majority of the electrodes in the 2-D array in order to simplify the analysis of spike propagation. Specifically, the following quantitative criteria are used to retain spikes: 1) a single negative-going threshold crossing followed by a single positive-going threshold crossing within the clipped 160 msec window surrounding the triggering threshold crossing; 2) at least 50% of electrodes have a root-mean-square (RMS) value >40% of the maximal RMS across channels (within the 160 msec window). 724 average spikes met the above criteria. FIG. 35 shows 16 representative detected spike waveforms.

Feature Selection:

For each of the 724 spikes, a 63-element feature vector is generated in the following manner: The average spike waveform is cross-correlated with each of the 360 single-channel spikes. This yields a single value per channel capturing the delay of the spike on each channel of the array. The zero-meaned RMS (i.e. standard deviation) for each channel is then calculated. This yielded a single value per channel capturing a representation of the power of the spike on each channel of the array. The 724×360 delay values are then normalized by dividing by each row maximum. The 724×360 ‘power’ values are similarly normalized. The 724×360 matrix of delay values is then concatenated with the 724×360 matrix of ‘power’ values. The result is a 724×720 matrix representing 724 spikes, each with 720 features. Principal components analysis (PCA) is performed and a number of dimensions accounting for >99% of the data variance is retained. This results in a dimensionality reduction from 720 to 63.

Clustering:

k-mediods clustering [4] is performed and the gap statistic [5] is used to determine the number of clusters, similar to methods applied in other EEG classification tasks [6]. 16 clusters of ST patterns are identified. FIG. 36 shows delay maps for spikes clustered closest in L1 distance to the centroid of each distinct cluster. Blue indicates electrodes with early delay values relative to the average spike waveform and red indicates electrodes with later delay values. All analysis is performed in the Matlab® environment (The Mathworks Inc., Natick, Mass., USA).

Statistical Testing:

We hypothesize that some ST patterns occur preferentially during seizure epochs. We use Pearson's chi-squared test to test the null hypothesis that the proportion of spikes occurring during seizure is equal across clusters (i.e. ST patterns). To address the issue of identifying a specific cluster which might account for a rejection of the null hypothesis, we conduct a permutation test. We hold cluster membership of each spike fixed while randomly permuting the seizure and non-seizure labels for one million permutations. For every permutation we record the maximum (over all clusters) of the proportion of seizure-spikes within each cluster to obtain the null distribution. We then compare the observed maximum to this null distribution.

Results:

FIG. 37 displays spike clustering and seizure analysis results. Each of the pies represents one of the 16 different spike ST patterns identified. Delay maps of representative spikes from each corresponding cluster are shown in FIG. 36. The whole area of each pie in FIG. 37 is scaled in proportion to the total number of spikes within the cluster. The blue section within each pie represents the percentage of spikes associated with non-seizure epochs. The white section within each pie represents the percentage of spikes recorded during seizures. Clusters 3 and 6 appear to have disproportionately large numbers of spikes occurring during seizure relative to outside.

We found a strong relationship between ST pattern and seizure state (i.e. within or outside of a seizure epoch). We reject the null hypothesis that the proportion of spikes occurring during seizure in each of the 16 clusters is the same (χ²(15, N=724)=415.1, p<<0.0001). Furthermore, we find the proportion of within-seizure spikes contained specifically in clusters 3 and 6 are significantly higher than would be expected by chance (p<<0.0001 for both clusters).

Discussion:

Our analysis indicates that two specific ST spike patterns correlate with seizure epochs. In addition, we found other ST spike patterns that appear to be more loosely associated with seizures. We believe those patterns indicate periods of transition from the interictal to ictal states. We believe these patterns may hold information about the progression of abnormal electrical activity as seizures approach. Analyzing the brain's electrical activity using any of the electrode arrays provided herein provide new opportunities to increase our understanding of epileptiform spikes and their patterns of propagation. One possibility raised by these waveforms, is that spikes and seizures, when viewed at this resolution, may have features in common with cardiac dysrhythmias. In this sense it may be precisely their multidimensional ST appearance that could reveal re-entrant patterns and triggers in the same way that these types of events occur in cardiac tissue. We next plan to study the relationship among waveforms leading into ictal events, those occurring periodically during seizures, and the waveforms that occur immediately prior to seizure cessation.

Recordings at this spatial scale may be important to clinical patient care and evaluation for epilepsy surgery, as evidenced by studies in humans of high frequency oscillations and microseizures that are poorly detected by standard clinical electrode systems[7,8]. We anticipate that this new electrode technology, combined with novel methods for analyzing the large, high-resolution data sets arising from it, may lead to better understanding of spike discharges and seizure development, and more effective therapies for the more than 33% of epilepsy patients who remain medically refractory.

REFERENCES

-   [1] P. K. Campbell, K. E. Jones, R. J. Huber, K. W. Horch, and R. a     Normann, “A silicon-based, three-dimensional neural interface:     manufacturing processes for an intracortical electrode array,” IEEE     transactions on bio-medical engineering, vol. 38, August 1991, pp.     758-68. -   [2] K. Morimoto, M. Fahnestock, and R. J. Racine, “Kindling and     status epilepticus models of epilepsy: rewiring the brain.,”     Progress in neurobiology, vol. 73, May. 2004, pp. 1-60. -   [3] J. Viventi, D.-H. Kim, J. D. Moss, Y.-S. Kim, J. a Blanco, N.     Annetta, a Hicks, J. Xiao, Y. Huang, D. J. Callans, J. a Rogers,     and B. Litt, “A Conformal, Bio-Interfaced Class of Silicon     Electronics for Mapping Cardiac Electrophysiology,” Science     Translational Medicine, vol. 2, 2010, pp. 24ra22-24ra22. -   [4] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of     Statistical Learning, New York, N.Y., USA: Springer-Verlag, 2001. -   [5] R. Tibshirani, G. Walther, and T. Hastie, “Estimating the number     of clusters in a data set via the gap statistic,” Journal of the     Royal Statistical Society: Series B (Statistical Methodology), vol.     63, May. 2001, pp. 411-423. -   [6] J. A. Blanco, M. Stead, A. Krieger, J. Viventi, W. R.     Marsh, K. H. Lee, G. a Worrell, and B. Litt, “Unsupervised     classification of high-frequency oscillations in human neocortical     epilepsy and control patients.,” Journal of neurophysiology, vol.     104, November 2010, pp. 2900-12. -   [7] G. a Worrell, A. B. Gardner, S. M. Stead, S. Hu, S.     Goerss, G. J. Cascino, F. B.

Meyer, R. Marsh, and B. Litt, “High-frequency oscillations in human temporal lobe: simultaneous microwire and clinical macroelectrode recordings.,” Brain: a journal of neurology, vol. 131, 2008, pp. 928-37.

-   [8] M. Stead, M. Bower, B. H. Brinkmann, K. Lee, W. R. Marsh, F. B.     Meyer, B. Litt, J. Van Gompel, and G. a Worrell, “Microseizures and     the spatiotemporal scales of human partial epilepsy,” Brain: a     journal of neurology, vol. 133, October 2010, pp. 2789-97.

STATEMENTS REGARDING INCORPORATION BY REFERENCE AND VARIATIONS

All references throughout this application, for example patent documents including issued or granted patents or equivalents; patent application publications; and non-patent literature documents or other source material; are hereby incorporated by reference herein in their entireties, as though individually incorporated by reference, to the extent each reference is at least partially not inconsistent with the disclosure in this application (for example, a reference that is partially inconsistent is incorporated by reference except for the partially inconsistent portion of the reference).

The terms and expressions which have been employed herein are used as terms of description and not of limitation, and there is no intention in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the invention claimed. Thus, it should be understood that although the present invention has been specifically disclosed by preferred embodiments, exemplary embodiments and optional features, modification and variation of the concepts herein disclosed may be resorted to by those skilled in the art, and that such modifications and variations are considered to be within the scope of this invention as defined by the appended claims. The specific embodiments provided herein are examples of useful embodiments of the present invention and it will be apparent to one skilled in the art that the present invention may be carried out using a large number of variations of the devices, device components, methods steps set forth in the present description. As will be obvious to one of skill in the art, methods and devices useful for the present methods can include a large number of optional composition and processing elements and steps.

International Application Publication WO 2009/114689 and U.S. patent application Ser. No. 12/892,001 (filed Sep. 28, 2010), which are hereby incorporated by reference in its entirety, disclose flexible and scalable sensor arrays for recording and modulating physiologic activity. US Patent Publication Nos. US 2008/0157235, US 2008/0108171, US 2010/0002402 and U.S. Pat. No. 7,557,367 issued Jul. 7, 2009, all of which are hereby incorporated by reference in their entireties, disclose multilayer stretchable, foldable and printable semiconductor devices.

When a group of substituents is disclosed herein, it is understood that all individual members of that group and all subgroups, including any isomers, enantiomers, and diastereomers of the group members, are disclosed separately. When a Markush group or other grouping is used herein, all individual members of the group and all combinations and subcombinations possible of the group are intended to be individually included in the disclosure. Specific names of compounds are intended to be exemplary, as it is known that one of ordinary skill in the art can name the same compounds differently.

Every formulation or combination of components described or exemplified herein can be used to practice the invention, unless otherwise stated.

Whenever a range is given in the specification, for example, a size, sensitivity, temperature, a time, data transfer rate, or a composition or concentration range, all intermediate ranges and subranges, as well as all individual values included in the ranges given are intended to be included in the disclosure. It will be understood that any subranges or individual values in a range or subrange that are included in the description herein can be excluded from the claims herein.

All patents and publications mentioned in the specification are indicative of the levels of skill of those skilled in the art to which the invention pertains. References cited herein are incorporated by reference herein in their entirety to indicate the state of the art as of their publication or filing date and it is intended that this information can be employed herein, if needed, to exclude specific embodiments that are in the prior art. For example, when composition of matter are claimed, it should be understood that compounds known and available in the art prior to Applicant's invention, including compounds for which an enabling disclosure is provided in the references cited herein, are not intended to be included in the composition of matter claims herein.

As used herein, “comprising” is synonymous with “including,” “containing,” or “characterized by,” and is inclusive or open-ended and does not exclude additional, unrecited elements or method steps. As used herein, “consisting of” excludes any element, step, or ingredient not specified in the claim element. As used herein, “consisting essentially of” does not exclude materials or steps that do not materially affect the basic and novel characteristics of the claim. In each instance herein any of the terms “comprising”, “consisting essentially of” and “consisting of” may be replaced with either of the other two terms. The invention illustratively described herein suitably may be practiced in the absence of any element or elements, limitation or limitations which is not specifically disclosed herein.

One of ordinary skill in the art will appreciate that starting materials, biological materials, reagents, synthetic methods, purification methods, analytical methods, assay methods, and biological methods other than those specifically exemplified can be employed in the practice of the invention without resort to undue experimentation. All art-known functional equivalents, of any such materials and methods are intended to be included in this invention. The terms and expressions which have been employed are used as terms of description and not of limitation, and there is no intention that in the use of such terms and expressions of excluding any equivalents of the features shown and described or portions thereof, but it is recognized that various modifications are possible within the scope of the invention claimed. Thus, it should be understood that although the present invention has been specifically disclosed by preferred embodiments and optional features, modification and variation of the concepts herein disclosed may be resorted to by those skilled in the art, and that such modifications and variations are considered to be within the scope of this invention as defined by the appended claims. 

We claim:
 1. A device for spatio-temporally electrically interfacing with a brain in a biological environment, the device comprising: a conformable substrate; a conformable electronic circuit comprising a deformable array of electrodes in electrical communication with a plurality of deformable electrical interconnects, the deformable array of electrodes supported by the conformable substrate; a barrier layer encapsulating at least a portion of the deformable electrical interconnects, wherein the conformable substrate, conformable electronic circuit and the barrier layer provide a net bending stiffness of the device low enough that the device establishes conformal contact with brain tissue in the biological environment; and a controller connected to the conformable electrical circuit to monitor or actuate a spatio-temporal profile over the surface of the brain in electrical contact with the plurality of electrodes.
 2. The device of claim 1, wherein each electrode is electrically connected to a pair of matched transistors, wherein the matched transistors comprise a multiplexing transistor and a buffer transistor.
 3. The device of claim 2, wherein each pair of matched transistors are electrically connected to a common constant current source and a current mirror.
 4. The device of claim 2, wherein the transistors are flexible and comprise single-crystal silicon.
 5. The device of claim 1, wherein adjacent electrodes are separated from each other by a separation distance selected from a range that is greater than or equal to 100 μm and less than or equal to 1 mm.
 6. The device of claim 5, wherein each electrode has an electrode surface area that is less than or equal to 0.2 mm².
 7. The device of claim 1, wherein the deformable array of electrodes is supported by a top surface of said barrier layer.
 8. The device of claim 7, wherein the electrodes are coated with an electrode coating layer, wherein said electrode coating layer comprises platinum.
 9. The device of claim 1 having a thickness, wherein the thickness is less than or equal to 30 μm.
 10. The device of claim 1, further comprising a support material, said support material having a first surface and a second surface opposed to said first surface, wherein a first portion of said electrodes are supported by said first surface, and a second portion of said electrodes are supported by said second surface.
 11. The device of claim 1, further comprising: a therapeutic device operably connected to the controller; and a receiving passage through the conformable substrate for receiving at least a portion of the therapeutic device; wherein the controller is configured to actuate the therapeutic device to provide a therapeutic intervention to the brain.
 12. The device of claim 11, wherein the therapeutic device is selected from the group consisting of a penetrating electrode, a micro-syringe and an optical fiber.
 13. The device of claim 11, wherein the receiving passage is centered at a center point of the conformable substrate, and the receiving passage has a cross-sectional area selected from a range that is greater than or equal to 100 μm² and less than or equal to 1 cm².
 14. The device of claim 1, wherein said deformable array of electrodes comprises alternating columns of actuating and monitoring electrodes.
 15. A brain diagnostic tool that provides electrical information at a transfer rate that is greater than or equal to 90,000 samples per second. 